OPTIMIZATION OF ETHANOL PRODUCTION FROM
CONCENTRATED SUBSTRATE
Except where reference is made to the work of others, the work described in this
dissertation is my own or was done in collaboration with my advisory committee.
This dissertation does not include proprietary or classified information.
Byung-Hwan Um
Certificate of Approval:
Y. Y. L e e
Professor
Chemical Engineering
Thomas R. Hanley, Chair
Professor
Chemical Engineering
Gopal A. Krishnagopalan
Professor
Chemical Engineering
R. Eric Berson
Assistant Professor
Chemical Engineering at
University of Louisville
Joe F. Pittman
Interim Dean
Graduate School
OPTIMIZATION OF ETHANOL PRODUCTION FROM
CONCENTRATED SUBSTRATE
Byung-Hwan Um
A Dissertation
Submitted to
the Graduate Faculty of
Auburn University
in Partial Fulfillment of the
Requirements for the
Degree of
Doctor of Philosophy
Auburn, Alabama
August 4, 2007
iii
OPTIMIZATION OF ETHANOL PRODUCTION FROM
CONCENTRATED SUBSTRATE
Byung-Hwan Um
Permission is granted to Auburn University to make copies of this dissertation at its
discretion, upon request of individuals or institutions and at their expense. The author
reserves all publication rights.
Signature of Author
Data of Graduation
iv
VITA
Byung-Hwan Um was born in Masan on the southern coast of Korea on March
16, 1971, the son of Tae-Joon Um and Kyung-Ja Yoo. After completing high school at
JoongAng High School in Masan, Korea in 1990, he attended Gyeongsang National
University in Chinju, South Gyeongsang Province. (The Republic of Korea has nine
provinces and thirty national universities). From 1992-1995, he joined his military
service at Wonju, Gangwon Province. He graduated with a Bachelor of Science degree
in 1998. He conducted research as a Research Assistant at Biochemical Laboratory at
Gyeongsang National University. He attended the Colorado State University in Fort
Collins, Colorado in the fall of 1999. He married Young-In Oh, daughter of Kil-Hwan
Oh and Ook-Soon Kim on May 27, 2000 and earned a Master of Science in the summer
of 2002. He then entered the University of Louisville in the spring of 2002. Following
this academic performance, he transferred the Graduate School at Auburn University in
the spring of 2004 to pursue the degree of Doctor of Philosophy in Chemical Engineering
v
DISSERTATION ABSTRACT
OPTIMIZATION OF ETHANOL PRODUCTION FROM
CONCENTRATED SUBSTRATE
Byung-Hwan Um
Doctor of Philosophy, August 4, 2007
(M.S. Chem. Eng., Colorado State University, 2002)
268 Typed Pages
Directed by Thomas R. Hanley
This research optimizes ethanol production from high concentrations of cellulosic
substrates in order to produce ethanol economically from renewable resources. This
study identifies and quantifies the factors that influence ethanol yield on high solids
biomass slurries during saccharification followed by fermentation (SFF) processes
leading to development of a computational fluid dynamics (CFD) model. This model
describes slurry rheology in terms of measurable parameters and slurry/biomass
characteristics as these parameters undergo transformation during the SFF process.
To obtain five percent (v/v) ethanol production needed for an economically
viable industrial-scale ethanol distillation, high carbonate concentration is required.
High carbonate concentration can be achieved only with high initial cellulose
concentration combined with a favorable conversion yield of cellulose into soluble sugars.
vi
Many researchers have reported repeatedly that solid concentrations above 10 percent
resulted in poor ethanol yield due to inefficient mass transfer and to the different
operating temperatures required for enzymatic hydrolysis and fermentation.
To develop data for a full scale design, ethanol fermentation of concentrated
Solka Floc is evaluated in a three-liter bioreactor. The effects of mixing are evaluated
using computational fluid dynamics (CFD) simulations of the three-liter reactor.
vii
ACKNOWLEDGMENTS
I would like to sincerely thank all the individuals who in one way or another
assisted me with the challenge that this work presented to me. Specifically, my great
appreciation goes to my advisor, Dr. Thomas R. Hanley, for intellectual support,
encouragement, and enthusiasm throughout my study, and for his patience in correcting
both my stylistic and scientific errors. The completion of this work would not have
been possible without my boss.
I would like to acknowledge with sincere gratitude to the members of my
dissertation committee, Dr. Y. Y. Lee, Dr. Gopal A. Krishnagopalan, and Dr. Eric Berson.
I am grateful for their helpful advices on various topics. Thanks also to Dr. Sung-Bae
Kim, for the opportunity he offered me previously that helped to conduct this research.
I would like to thank my brother in law Dr. Cha-Su Ahn, my sister Hye-Sun Um,
for their constant support and encouragement which have motivated me through my
entire endeavor. The author wish to thank Dr. Teak-Keun Kim, Dr. Moon-Seong Kang,
and Mr. Rajesh K. Dasari for their valuable discussion and the support of this project.
During my stay in USA, my thoughts were never far away from my parents, Tae-
Joon Um, Kyung-Ja Yoo, and parents in law, Kil-Hwan Oh, Oak-Soon Kim in Korea.
Finally, this dissertation is dedicated to my wife, Young-In Oh. I want to thank
my lovely wife for her assistance and faith in me, and thanks to my best friends, Su-Man
Paeng, Sang-Hwan Lee, and Hyung-Wook Kim.
viii
Style manual or journal used Chemical Engineering (Biochemical Technology) ____
_______________________________________________________________________
Computer software used Microsoft Office XP (Professional) __________________
_______________________________________________________________________
ix
TABLE OF CONTENTS
LIST OF TABLES.................................................................................................................xi
LIST OF FIGURES .............................................................................................................xiv
I. INTRODUCTION............................................................................................................1
Objectives .....................................................................................................................5
II. LITERATURE REVIEW................................................................................................6
THE BIOMASS-TO-ETHANOL PROCESS OVERVIEW ........................................6
BIOETHANOL AS FUEL............................................................................................7
Ethanol and its properties........................................................................................8
What are the benefits of bioethanol ? .....................................................................9
Bioethanol production from biomass....................................................................10
LIGNOCELLULOSIC BIOMASS.............................................................................10
Structure of biomass .............................................................................................11
Cellulose ...........................................................................................................11
Hemicellulose ...................................................................................................13
Lignin................................................................................................................13
Solka Floc .............................................................................................................14
PRETREATMENT IMPORTANCE..........................................................................14
PRETREATMENT TECHNIQUES...........................................................................15
ENZYMATIC HYDROLYSIS...................................................................................17
ETHANOL FERMENTATION..................................................................................18
ETHANOL FERMENTATION PROCESS ...............................................................20
Direct Microbial Conversion (DMC)....................................................................20
Separate Hydrolysis and Fermentation (SHF) ......................................................21
Simultaneous Saccharification and Cofermentation (SSFC)................................21
Simultaneous Saccharification and Fermentation (SSF) ......................................22
Theoretical Ethanol Yield via Fermentation Process............................................23
BIOREACTOR DESIGN ...........................................................................................24
Mixing in Bioreactors ...........................................................................................25
Reactor Geometries...........................................................................................26
Turbine Agitatiors.............................................................................................27
Helical-Ribbon and Anchor Impellers..............................................................30
Conventional Radical Flow Turbine.................................................................32
x
Flow Patterns ....................................................................................................32
Scale-Up Method For Fermentors ........................................................................34
Simulation Using Computational Fluid Dynamics...............................................37
Physical Model for Turbulence Fluid Motion ......................................................42
Approaches for Mixing Tank Simulation .............................................................45
Structured and Unstructured Mesh .......................................................................47
Multiphase Simulation..........................................................................................47
VISCOSITY OF CELLULOSIC SLURRIES ............................................................48
Non-Newtonian Behavior .....................................................................................49
Measurement Techniques .....................................................................................51
Impeller Ribbon Viscometer Technique...........................................................54
Ruston Impeller Programmed Viscometer........................................................55
III. HIGH SOLID ENZYMATIC HYDROLYSIS AND FERMENTATION OF
SOLKA FLOC TO ETHANOL ............................................................................................56
ABSTRACT................................................................................................................56
INTRODUCTION ......................................................................................................58
MATERIAL AND METHODS..................................................................................61
Raw Material.........................................................................................................61
Commercial Enzyme.............................................................................................61
Microorganism......................................................................................................61
Bench Scale Reactor .............................................................................................62
Strategy of High Solid Loading on Enzyme Hydrolysis and Fermentation .........63
Enzymatic Hydrolysis...........................................................................................63
Cell Stock Culture.................................................................................................65
Preliminary Fermentation .....................................................................................66
2 L Fermentation...................................................................................................66
ANALYSIS AND ASSAY.........................................................................................68
Carbohydrate.........................................................................................................68
Moisture and Ash..................................................................................................68
Dry Cell Weight vs Optical Density .....................................................................69
Glucose and Ethanol .............................................................................................70
RESULTS AND DISCUSSION.................................................................................71
Enzymatic Hydrolysis Below 10 % (w/v) ............................................................71
Stirring Power in 3L Fully-Baffled Tanks............................................................75
The Effect of Bioreactor Configuration on Bioconversion...................................78
The Effect of Rotational Speed on Glucose Yield................................................85
High-Solids Saccharification by Portion Loading ................................................88
The Effects of Temperature on High-Solid Bioconversion ..................................92
The Effect of Substrate Concentration on Ethanol Yield .....................................95
CONCLUSIONS.........................................................................................................98
IV. RHEOLOGICAL PARAMETER DETERMINATION FOR ENZYMATIC
SUSPENSIONS AND FERMENTATION BROTHS WITH HIGH SUBSTRATES
LOADING?? ..................................................................................................................100
xi
ABSTRACT..............................................................................................................100
INTRODUCTION ....................................................................................................102
MATERIAL AND METHODS................................................................................104
Suspension Fluids used for Measurements.........................................................104
Viscometer ..........................................................................................................104
Concentric Cylinder System ...............................................................................104
Measurement of Particle Size .............................................................................106
Calculation of Power Law Parameters................................................................106
Yield Stress .........................................................................................................107
RESULTS AND DISCUSSION...............................................................................108
Rheological Behavior of Enzymatic Hydrolysis Suspension .............................108
Rheological Parameter Estimation for Psudoplastic Suspension........................112
Determination of Particle Size Treated by Enzyme............................................126
CONCLUSION.........................................................................................................131
V. FLOW PATTERN SIMULATION IN A HIGH SOLIDS CELLULOSE TO
ETHANOL BIOREACTOR USING COMPUTATIONAL FLUID
DYNAMICS???.. .........................................................................................................132
ABSTRACT..............................................................................................................132
INTRODUCTION ....................................................................................................134
MATERIAL AND METHODS................................................................................136
Tank Geometric Configuration of the Investigated Vessel.................................136
The k-? Mathematical Models ............................................................................137
Constant N
p
and P per Liquid Volume................................................................138
Simulation Model................................................................................................138
Simulation Tool Package ....................................................................................138
Description of Supercomputer ............................................................................139
RESULTS AND DISCUSSION...............................................................................140
Vessel Geometry and Grid Generation...............................................................140
Convergence Criteria and Blend Time ...............................................................142
Grid Refinement..................................................................................................143
Power Law Flow Behavior Index (0.46?n?0.97) ...............................................148
Turbulence a Tank with Baffled Rushton Impeller ............................................148
Predicted Velocity Distribution ..........................................................................156
Axial Velocity.....................................................................................................157
Stagnant and Slow Flow Zone ............................................................................166
Shear Stress and Turbulent Viscosity in Mixing Tank .......................................173
Turbulence Kinetic Energy and Dissipation Rate...............................................174
CONCLUSION.........................................................................................................183
VI. OVERALL CONCLUSIONS AND RECOMMENDATIONS ...................................185
BIBLIOGRAPHY...............................................................................................................191
APPENDICES.. ..................................................................................................................202
xii
APPENDIX A...........................................................................................................202
MEDIA FOR FERMENTATION AND BIOREACTOR PROTOCOLS ........202
A-1. Agar media compositios .........................................................................203
A-2. Revival and growth of Zymomonas mobilis 39679 pZB 4L...................205
A-3. Set up of BioFlo 3000 (New Brunswick Scientific)...............................209
APPENDIX B ...........................................................................................................215
RHEOLOGY AND RHEOLOGICAL PARAMETER DETERMINATION
???.................................................................................................................215
APPENDIX C ...........................................................................................................234
SIMULATION METHODS, PROCEDURES, AND APPARATUS ................234
C-1. Start Mixism and Fluent..........................................................................235
xiii
LIST OF TABLES
Table II-1 Important physical properties of ethanol ........................................................9
Table II-2 Methods used for pretreatment of lignocellulosics.......................................16
Table II-3 K-values for effective shear-rate model........................................................28
Table II-4 Scale-up criteria in fermentation industries..................................................36
Table II-5 Overview of Turbulence Models ..................................................................44
Table III-1 Initial composition of untreated Solka Floc ..................................................61
Table III-2 Ethanol yield and conversion percent for Zm. mobilis after 48 hours...........96
Table IV-1 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (10 percent, w/v) ..............................................116
Table IV-2 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (15 percent, w/v) ..............................................117
Table IV-3 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (20 percent, w/v) ..............................................118
Table IV-4 Determination of rheological parameter as function of time during initial
SSF process.................................................................................................120
Table IV-5 Determination of rheological parameter as function of time during initial
SFF process.................................................................................................121
Table IV-6 Determination of rheological parameter as function of time during initial
Fermentation process ..................................................................................130
Table B-1 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (10 percent, w/v, 30 FPU, 50
o
C).....................225
xiv
Table B-2 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (10 percent, w/v, 15 FPU, 50
o
C).....................226
Table B-3 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (10 percent, w/v, 30 FPU, 30
o
C).....................227
Table B-4 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (15 percent, w/v, 30 FPU, 50
o
C).....................228
Table B-5 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (15 percent, w/v, 15 FPU, 50
o
C).....................229
Table B-6 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (15 percent, w/v, 30 FPU, 30
o
C).....................230
Table B-7 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (20 percent, w/v, 30 FPU, 50
o
C).....................231
Table B-8 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis (20 percent, w/v, 15 FPU, 50
o
C).....................232
Table B-9 Determination of rheological parameter as function of time during initial
4-hr enzymatic hydrolysis 20 percent, w/v, 30 FPU, 30
o
C) ......................233
Table C-1 Geometry of 3L mixing tank ......................................................................242
Table C-2 Blend time and flow rate (RPM=120) ........................................................243
Table C-3 Power draw and correlation for water simulation (?=0.0010, RPM=120).244
Table C-4 Power draw and correlation for 10 % Solka Floc fermentation broth
(?=0.0192, RPM=120)................................................................................245
Table C-5 Power draw and correlation for 15 % Solka Floc fermentation broth
(?=0.0775, RPM=120)................................................................................246
Table C-6 Power draw and correlation for 20 % Solka Floc fermentation broth
(?=0.1050, RPM=120)................................................................................247
xv
LIST OF FIGURES
Figure II-1 Basic biomass-to-ethanol flow diagram ........................................................6
Figure II-2 A simplified model to illustrate the cross-linking of cellulose micro fibrils
and hemicellulose in the lignocellulosic biomass.......................................12
Figure II-3 The standard geometry of mixing tank; nomenclature used to describe the
mixing system ...............................................................................................26
Figure II-4 A wide variety of turbine is available to handle viscosities to about
50 pa-s.........................................................................................................27
Figure II-5 Helical-ribbon and anchor impellers provide an alternative to turbine
impeller .......................................................................................................30
Figure II-6 Streamline pattern in a standard cylindrical system with axial high-speed
impeller and radial baffles ..........................................................................33
Figure II-7 Streamline patterns in cylindrical system with axial high-speed impeller
and draft tube ..............................................................................................33
Figure II-8 Streaklines showing flow around a 2d bluff body.......................................45
Figure II-9 Sliding Grid Motion ....................................................................................46
Figure III-1 Strategy of high solid loading on enzyme hydrolysis and fermentation......64
Figure III-2 The glucose conversion rate during the initial 6-hr enzymatic hydrolysis
of 5 percent (w/v) Solka Floc........................................................................72
Figure III-3 Enzymatic hydrolysis of Solka Floc at lower percent solid concentration
(III-3a) and Solka Floc at 5 percent solid for different impeller type (III-
3b) as a function of time at constant cellulase activity .................................74
Figure III-4 Power consumption in 2 L bioreactor with 5 percent (w/v) Solka Floc
suspension with various bioreactor configuration as function of RPM........76
xvi
Figure III-5 Power consumption in 2 L bioreactor with 15 percent (w/v) Solka Floc
suspension with various bioreactor configuration as function of RPM........77
Figure III-6 The effect of baffles on enzymatic hydrolysis of Solka Floc at 5 percent
(III-6a) and 10 percent solid concentration with Rushton impeller as a
function of time at constant cellulose activity ..............................................80
Figure III-7 The glucose conversion as a function of time for the two bioreactor:
Baffled Rushton and Baffled Marine Configuration at 5 percent (w/v)
Solka Floc .....................................................................................................81
Figure III-8 The effect of baffles on enzymatic hydrolysis of Solka Floc at 13 % (III-
6a) and 15 % solid concentration with Rushton impeller as a function of
time at constant cellulase activity .................................................................82
Figure III-9 The effect of RPM on enzymatic hydrolysis of Solka Floc at 10 (III-8a),
13 (III-8b) and 15 percent (III-8c) solid concentration with baffled
Rushton bioreactor as a function of time at constant cellulose activity........84
Figure III-10 The enzymatic hydrolysis for the baffled Rushton (III-9a) and Marine
(III-9b) impellers as a function of time at constant cellulose activity ..........87
Figure III-11 The effects of temperature on high solid enzyme hydrolysis ......................90
Figure III-12 The effects of temperature on cell growth curve and ethanol production
.....................................................................................................................91
Figure III-13 The time course of substrate utilization and ethanol production by
Zymomonas mobilis at 10 (III-13a), 15 (III-13b), and 20 percent (III-13c)
solid concentration with Rushton impeller as a function of time at constant
cellulase activity............................................................................................94
Figure III-14 The ethanol conversion yield (CEtOH) with Rushton impeller as a
function of retention time during SFF at 30
o
C (III-14a) and 40
o
C (III-14b)
with 10, 15, and 20 percent substrate concentration constant cellulase
activity. Note: same as condition of Figure III-13........................................97
Figure IV-1 Scheme of concentric cylinder (stirrer FL 100/6W) system of Paar
Physica modular compact rhoemeter (MCR 300) ......................................105
Figure IV-2 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hours enzymatic hydrolysis (10 percent, w/v)...........109
Figure IV-3 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hoursenzymatic hydrolysis (15 percent, w/v)............110
xvii
Figure IV-4 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hoursenzymatic hydrolysis (20 percent, w/v)............111
Figure IV-5 Viscosity and shear stress curves as a function of shear rate for different
time during initial SSF process (40
o
C) ......................................................113
Figure IV-6 Viscosity and shear stress curves as a function of shear rate for different
time during initial SFF process (combined temperature 50
o
C -30
o
C) ......114
Figure IV-7 Comparison of the different rheological models used to fit the shear stress
as function of shear rate data of 10 percent sold concentration of
fermentation broth at t=0. Symbols represent experimental measurements
and lines represent four different model predictions ..................................122
Figure IV-8 Comparison of the different rheological models used to fit the shear stress
as function of shear rate data of 15 percent sold concentration of
fermentation Broth at t=0. Symbols represent experimental measurements
and lines represent four different model predictions ..................................123
Figure IV-9 Comparison of the different rheological models used to fit the shear stress
as function of shear rate data of 20 percent sold concentration of
fermentation broth at t=0. Symbols represent experimental measurements
and lines represent four different model predictions ..................................124
Figure IV-10 Percentage (10a) volume and (10b) cumulative volume particle size
distribution for the substrate during SSF and SFF process (10 percent,
w/v) .............................................................................................................127
Figure IV-11 Prcentage (10a) volume and (10b) cumulative volume particle size
distribution for the substrate during SSF and SFF process (15 percent,
w/v) .............................................................................................................128
Figure IV-12 Percentage (10a) volume and (10b) cumulative volume particle size
distribution for the substrate during SSF and SFF process (20 percent,
w/v) .............................................................................................................129
Figure V-1 NBS 3 L bioreactor tank geometry ...........................................................136
Figure V-2 Nomenclature used to describe the mixing system ...................................140
Figure V-3 Grid edge (3 L Mixing Tank)....................................................................144
Figure V-4 Outline (3 L Mixing Tank)........................................................................145
Figure V-5 Grid face (3 L Mixing tank) ......................................................................146
xviii
Figure V-6 Sweep surface in mixing tank ...................................................................147
Figure V-7 Velocity vectors colored by velocity magnitude (m/s, 10 percent
suspension)................................................................................................149
Figure V-8 Contour of velocity magnitude-panel 2, 4, and 6 (m/s, 10
percent suspension)...................................................................................150
Figure V-9 Contour of velocity magnitude-panel 3 and 5 (m/s, 10
percent suspension)...................................................................................151
Figure V-10 Contours of axial velocity (m/s, 10 percent suspension)-panel 1 .............152
Figure V-11 Contour of turbulence kinetic energy (k, m2/s2, 10 percent suspension)-
panel 1.......................................................................................................153
Figure V-12 Contour of turbulent dissipation rate (k, m2/s3, 10 percent suspension)-
panel 1.......................................................................................................154
Figure V-13 Contour of turbulent viscosity (kg/m-s, 10 percent suspension)-panel 1
.....................................................................................................................155
Figure V-14 Velocity vectors colored by velocity magnitude (m/s, 15 percent
suspension) - panel 1.................................................................................159
Figure V-15 Velocity vectors colored by axial velocity (m/s, 15 percent suspension)-
panel 7.......................................................................................................160
Figure V-16 Contours of axial velocity (m/s, 15 percent suspension)-panel 1 .............161
Figure V-17 Velocity vectors colored by velocity magnitude (m/s, 15 percent
suspension)- impeller and panel 1.............................................................162
Figure V-18 Contour of turbulence kinetic energy (k, m2/s2, 15 percent suspension)-
panel 1.......................................................................................................163
Figure V-19 Contour of turbulent dissipation rate (k, m2/s3, 15 percent suspension)-
panel 1.......................................................................................................164
Figure V-20 Contour of turbulent viscosity (kg/m-s, 15 percent suspension)-panel 1
.....................................................................................................................165
Figure V-21 Velocity vectors colored by velocity magnitude (m/s, 20 percent
suspension)- panel 1..................................................................................167
xix
Figure V-22 Velocity vectors colored by velocity magnitude (m/s, 20 percent
suspension)-bottom of panel 6..................................................................168
Figure V-23 Contours of axial velocity (m/s, 20 percent suspension)-panel 1 .............169
Figure V-24 Contour of turbulence kinetic energy (k, m2/s2, 20 percent suspension)-
panel 1.......................................................................................................170
Figure V-25 Contour of turbulent dissipation rate (k, m2/s3, 20 percent suspension)-
panel 1.......................................................................................................171
Figure V-26 Contour of turbulent viscosity (kg/m-s, 20 percent suspension)-panel 1
.....................................................................................................................172
Figure V-27 Average of axial velocity of 2 L suspension as tank radial at panel 1 ......175
Figure V-28 Average of axial velocity of 2 L suspension as tank radial at panel 2 ......175
Figure V-29 Average of axial velocity of 2 L suspension as tank radial at panel 4 ......176
Figure V-30 Average of axial velocity of 2 L suspension as tank radial at panel 6 ......176
Figure V-31 Average of axial velocity of 2 L suspension as tank radial at panel 7 ......177
Figure V-32 Average of shear stress of 2 L suspension as tank radial at panel 1 .........177
Figure V-33 Average of shear stress of 2 L suspension as tank radial at panel 3 .........178
Figure V-34 Average of shear stress of 2 L suspension as tank radial at panel 4 .........178
Figure V-35 Average of shear stress of 2 L suspension as tank radial at panel 5 .........179
Figure V-36 Average of turbulent kinetic energy (k) of 2 L suspension as tank radial at
panel 1.......................................................................................................179
Figure V-37 Average of turbulent kinetic energy (k) of 2 L suspension as tank radial at
panel 3.......................................................................................................180
Figure V-38 Average of turbulent kinetic energy (k) of 2 L suspension as tank radial at
panel 5.......................................................................................................180
Figure V-39 Average of turbulent dissipation rate (?) of 2 L suspension as tank radial
at panel 1...................................................................................................181
Figure V-40 Average of turbulent dissipation rate (?) of 2 L suspension as tank radial
at panel 3...................................................................................................181
xx
Figure V-41 Average of turbulent dissipation rate (?) of 2 L suspension as tank radial
at panel 5...................................................................................................182
Figure VI-1 Schematic diagram of continuous saccharification and fermentation at
separate temperature condition ...................................................................189
Figure A-1 Preparation and representative streak plate...............................................208
Figure B-1 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (10 percent, w/v, 30 FPU, 50
o
C) ...............................................................................................................216
Figure B-2 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (10 percent, w/v, 15 FPU, 50
o
C) ...............................................................................................................217
Figure B-3 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (10 percent, w/v, 15 FPU, 30
o
C) ...............................................................................................................218
Figure B-4 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (15 percent, w/v, 30 FPU, 50
o
C) ...............................................................................................................219
Figure B-5 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (15 percent, w/v, 15 FPU, 50
o
C) ...............................................................................................................220
Figure B-6 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (15 percent, w/v, 30 FPU, 30
o
C) ...............................................................................................................221
Figure B-7 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (20 percent, w/v, 30 FPU, 50
o
C) ...............................................................................................................222
Figure B-8 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (20 percent, w/v, 15 FPU, 50
o
C) ...............................................................................................................223
Figure B-9 Viscosity and shear stress curves as a function of shear rate for different
time during initial 4-hr enzymatic hydrolysis (20 percent, w/v, 30 FPU, 30
o
C) ...............................................................................................................224
Figure C-1 The graphical user interface (GUI) Components (MixSim 2.1.10)...........240
xxi
Figure C-2 Scaled residuals .........................................................................................241
Figure C-3 Histogram of tank cell equiangle skew .....................................................241
1
I. INTRODUCTION
In August 2005, President George W. Bush signed into law the Energy Policy Act
(EPACT) of 2005, creating a national Renewable Fuels Standard (RFS). This watershed
legislation establishes a baseline for renewable fuel use, beginning with 4 billion gallons
per year in 2006 and expanding to 7.5 billion gallons by 2012. The vast majority of the
renewable fuel used will be ethanol, resulting in a doubling of the domestic ethanol
industry in the next six years.
The United States and other industrialized countries of the world are dependent on
imported oil, and oil imports continue to increase, threatening the strategic security of
these countries (USA Today, 2005). For instance, the United States imports almost 60
per cent of its current oil supply (RFA, 2003). The transportation sector in the United
States is particularly dependent on oil with around 97 per cent of transportation energy
being derived from petroleum. Few substitutes exist for petroleum for transportation
usage (Wyman et al., 1993).
One of the most immediate and important applications of biological energy
systems is in the production of ethanol from biomass. Ethanol could reduce vehicle
pollution by as much as 54 per cent. Currently, 1.5 billion gallons of ethanol are added
to gasoline in the United States each year to improve vehicle performance and reduce air
2
pollution (Montross et al., 2004). While having these advantages, alcohol fuel still
cannot be used extensively due to limitations in technology, economic and regional
considerations, Since ethanol can be fermented and distilled from biomass, it is
considered to be a renewable energy source. Environmentally, ethanol blended with
gasoline is better than pure gasoline because of its renewability and lower toxicity (RSA,
2003).
The bioconversion of lignocellulosic feed stocks to fuel-grade ethanol offers a
means to alleviate and/or mitigate some of the environmental impacts of the petroleum
based transportation sector, such as the generation of airborne pollutants and greenhouse
gases, while concurrently reducing the amount of biomass that is land-filled. Various
feedstocks, including hardwoods, softwoods, and agricultural residues have been
evaluated for their potential as a feedstock for bioconversion. Numerous researchers
have investigated the production of ethanol from various lignocellulosic materials (Sun
and Cheng, 2002). Of the agricultural residues, corn stover is the most abundant with
annual US production of 150 million tons per year (Kadam and McMillan, 2003).
The polysaccharide fraction of agricultural residues can be hydrolyzed using
acids or enzymes as catalysts (Zhang et al., 1999). Cellulases catalyze the hydrolysis of
cellulose, the major structural component of biomass and the most abundant organic
material on earth (Scott et al., 1994). Complete hydrolysis of cellulose yields the easily
fermentable sugar, allowing biomass to be a potential renewable energy source (Fein et
al., 1991; RSA, 2003; Scott et al., 1994). As a result, there is strong interest in
understanding the process of enzymatic cellulose degradation (Fein et al., 1991). It is
well known that enzymatic hydrolysis of cellulosic biomass is severely hampered by the
3
crystallinity of the cellulose, the noncellulosic fraction, and the presence of end-products
during hydrolysis (McMillian, 1994). The problem is compounded by the relatively
small pore sizes in untreated substrates, creating mass transfer limitations on both
microorganisms and hydrolytic enzymes (Grohmann et al., 1985). Therefore, effective
pretreatment is an essential prerequisite to improve the rate and yields of saccharification.
With regard to the fermentation step, several strategies have been investigated to
obtain high ethanol yields. One leading approach is the simultaneous saccharification
and fermentation (SSF) process (Cheung and Anderson, 1997; ?hgren et al., 2006,;
Takagi et al., 1977). Typically, as much as 90 per cent or more of the fermentation
broth is water, which must be removed. Water separation is not only costly but also
produces a large aqueous stream that must then be disposed of or recycled. Integrative
approaches to water reduction include increasing the biomass concentration. When
concentrated slurries are processed during SSF, the high viscosity prevents efficient
mixing. It has been reported repeatedly that solid concentrations above 10 per cent
result in poor ethanol yields due to inefficient mass transfer (L?bbert and J
?
rgensen, 2001,
Mohagheghi et al., 1992; Spindler et al., 1988). Numerous attempts have been made to
enhance the fermentation under high solid substrate (?hgren et al., 2006; Philippidis and
Hatzis, 1997; Stenberg, 2000; Teymouri, 2005; Varga et al., 2004). This difficulty partly
accounts for the lack of literature concerning fermentation of biomass suspensions more
concentrated than 10 per cent (Philippidis and Hatzis, 1997).
An SSF process at high solids concentrations using both enzymes and
recombinant bacteria (for xylose conversion) appears to be the simplest and most
economically viable way to attain suitable ethanol concentrations in the broths for
4
distillation. On the whole, several process parameters must be optimized: high initial
substrate concentration, enzyme-to-substrate ratio, dosage of the active components (?-
glucosidase-to-cellulase ratio) in the enzymatic mixture, bacteria concentration, and
reactor conditions.
The process of designing, constructing and evaluating bioreactors for the high-
substrate concentration fermentation is both costly and time-consuming in industrial
fields (Oldshue, 1983). The use of computational fluid dynamics (CFD) can aid in
bioreactor development by providing detailed information on the hydrodynamic and
chemical environments necessary for optimal hydrolysis and cell growth.
Agitation in bioreactors is an important process design factor that can influence
the hydrolysis operation in several ways. Considering the heterogeneity of the
hydrolysis reaction environment where a liquid enzyme acts on a solid substrate,
adequate mixing is required to ensure sufficient contact between the reactants as well as
to promote heat and mass transfer within the reaction vessel. Moreover, it has been
shown that excessive mixing can deactivate the enzyme and microorganism reducing
production (sugar/ethanol) yields, owing to the shear force generated by the mixer and
the entrapment of air bubbles into the medium at the air liquid surface (Reese, 1980;
Ursula, 2002). Therefore, one way of improving the problems of the overall process is
to determine the optimum level of mixing and reactant amount to minimize the extent of
shear-induced enzyme and microorganism deactivation and to lower the mixing energy
costs.
5
Objectives
Thus, the overall objectives of the research were developed to answer definitely
the following issues
z To design and optimize a high solid slurry fermentation using commercial
enzymes and recombinant microorganisms.
z To investigate ethanol yield using high-solids, concentrated Solka-Floc slurries (>
10 per cent w/v).
z To determine a fundamental understanding of the rheology of high solids biomass
slurries during enzymatic hydrolysis.
z To develop fluid dynamic models to assist in full-scale design.
6
II. LITERATURE REVIEW
THE BIOMASS-TO ETHANOL PROCESS OVERVIEW
Lignocellulosic biomass can be converted to ethanol by acid or enzymatic
approaches. In either option, the material must first be processed in some way to reduce
its size and facilitate subsequent handing. Then, acids or enzymes are used to break
apart or hydrolyze the hemicellulose and cellulose chains to form their component sugars.
These sugars are fermented to bioethanol by adding yeasts, bacteria, or other suitable
microorganisms, and the ethanol is recovered by distillation or other separation
technologies for use as fuel. A process overview may be found in Figure II-1. This
figure is a basic biomass-to-ethanol flow diagram (Wayman et al., 1993)
Figure II-1. Basic Biomass-to-Ethanol Flow Diagram (Wayman et al., 1993)
Ac id-Hydrolysis
Pretreatment
Enzymatic
Hydrolysis
Glucose
Fermentation
Product
Recovery
Lignin
Processing
Xylose
Fermentation
Chemicals
Feedstock, Booster,
or Boiler Fuel
Ethanol
Xylose and Other Sugars
Lignin
Lignocellulosic
Biomass
7
BIOETHANOL AS FUEL
Biofuel is a generic term for any liquid fuel produced from sources other than
mineral reserves such as oil, coal and gas (EECA, 2005). In general biofuels can be
used as a substitute for, or an additive to, gasoline and diesel fuel in most transport and
non-transport applications. The most commonly used biofuels are biodiesel and
bioethanol.
The use of ethanol as an automobile fuel in the United States dates as far back
as 1908, to the Ford Model T. Henry Ford was a supporter of home-grown renewable
fuels, and his Model T could be modified to run on either gasoline or pure alcohol
(Carson, 2005). Ethanol was used to fuel cars well into the 1920s and 1930s as several
efforts were made to sustain a U.S. ethanol program. Standard Oil marketed a 25 per
cent ethanol by volume gasoline in the 1920s in the Baltimore area.
Utilizing cellulose to synthesize alternative renewable transportation fuels such as
ethanol to replace gasoline is a technology that can provide a permanent solution to our
energy needs (Wyman, 1994). Bioethanol is produced from the fermentation of sugar
by enzymes produced from specific varieties of yeast. The five major sugars are the
five-carbon xylose and arabinose and the six-carbon glucose, galactose, and mannose
(Wyman, 1996). Traditional fermentation processes rely on yeasts that convert six-
carbon sugars to ethanol. Glucose, the preferred form of sugar for fermentation, is
contained in both carbohydrates and cellulose. Because carbohydrates are easier than
cellulose to convert to glucose, the majority of ethanol currently produced in the United
States is made from corn, which supplies large quantities of carbohydrates. Also, the
8
organisms and enzymes for carbohydrate conversion and glucose fermentation on a
commercial scale are readily available.
Ethanol and Its Properties
Ethanol or ethyl alcohol, CH
3
CH
2
OH, has been described as one of the most
exotic synthetic oxygen-containing organic chemicals because of its unique combination
of properties as a solvent, a germicide, a beverage, an antifreeze, a fuel, a depressant, and
especially because of its versatility as a chemical intermediate for other organic
chemicals (U.S. Department of Energy, 2005).
Ethanol under ordinary condition is a volatile, flammable, clear, colorless
liquid. Its odor is pleasant, familiar, and characteristic, as is its taste when it is suitably
diluted with water. The physical and chemical properties of ethanol are primarily
dependent upon the hydroxyl group. This group imparts polarity to the molecule and
also gives rise to intermolecular hydrogen bonding. In the liquid state, hydrogen bonds
are formed by the attraction of the hydroxyl hydrogen of one molecule and the hydroxyl
oxygen of a second molecule. The effect of this bonding is to make liquid alcohol
behave as though it were largely dimerized. This behavior is analogous to that of water,
which is more strongly bonded and appears to exist in liquid clusters of more than two
molecules.
The chemistry of ethanol is largely that of the hydroxyl group, namely,
reactions of dehydration, dehydrogenation, oxidation, and esterification. The hydrogen
atom of the hydroxyl group can be replaced by an active metal, such as sodium,
9
potassium, and calcium, to form a metal ethoxide (ethylate) with the evolution of
hydrogen gas. Table II-1 lists the physical properties of ethanol.
Table II-1. Important physical properties of ethanol.
Property Value
Normal Boiling Point (
o
C) 78.32
Critical Temperature (
o
C) 243.1
Density (g/mL) 0.789
Energy Density (MJ/kg) 25.0
Auto-Ignition Temperature (
o
C) 793.0
Flammable Limits in Air
Lower, (vol %) 4.3
Upper, (vol %) 19.0
* Heat of combustion at 25?C, J/g 29676.69
What are the benefits of Bioethanol?
Bioethanol has a number of advantages over conventional fuels. It comes
from a renewable resource (i. e., crops) and not from a finite resource. These crops
typically can grow well in the United States (U.S. Department of Energy, 2001).
Another benefit over fossil fuels is the greenhouse gas emissions. The road transport
network accounts for 22 per cent of all greenhouse gas emissions. Through the use of
bioethanol, some of these emissions will be reduced as the growing fuel crops absorb
carbon dioxide (Tyson et al., 1993). Also, blending bioethanol with gasoline will help
extend the life of the oil supplies in the United States and ensure greater fuel security,
avoiding heavy reliance on oil producing nations. By encouraging bioethanol use, the
rural economy would also receive a boost from growing the necessary crops.
Bioethanol is also biodegradable and far less toxic than fossil fuels. In addition, using
bioethanol in older engines can help reduce the amount of carbon monoxide produced by
10
the vehicle, thus improving air quality. Another advantage of bioethanol is the ease
with which it can be integrated into the existing road transport fuel system. In quantities
up to 5 per cent, bioethanol can be blended with conventional fuel without the need of
engine modifications. Bioethanol is produced using familiar methods, such as
fermentation, and it can be distributed using the existing gasoline and transportation
systems.
Bioethanol Production from Biomass
Ethanol can be produced from biomass by the hydrolysis and sugar
fermentation processes. Biomass wastes contain a complex mixture of carbohydrate
polymers from the plant cell walls known as cellulose, hemicellulose and lignin (Um,
2002). In order to produce sugars from the biomass, the biomass is pre-treated with
acids or enzymes in order to reduce the size of the feedstock and to open up the plant
structure. The cellulose and the hemicellulose portions are broken down (hydrolyzed)
by enzymes or dilute acids into sucrose that is then fermented into ethanol (Wyman,
1996). The lignin which is also present in the biomass is normally used as a fuel for the
ethanol production plants boilers. There are three principle methods of extracting
sugars from biomass: concentrated acid hydrolysis, dilute acid hydrolysis and
enzymatic hydrolysis.
LIGNOCELLULOSIC BIOMASS
The structural materials that plants produce to form the cell walls, leaves, stems,
stalks, and woody portions of biomass are composed mainly of cellulose, hemicellulose,
11
and lignin (Fan et al., 1987). Together, they are called lignocellulose, a composite
material of rigid cellulose fibers embedded in a cross-linked matrix of lignin and
hemicellulose that bind the fibers. Lignocellulose plant structures also contain a variety
of plant-specific chemicals in the matrix, called extractives (resins, phenolics, and other
chemicals), and minerals (calcium, magnesium, potassium, and others) that will leave ash
when biomass is burned (WBDI, 2004).
Lignocellulosic materials are underutilized by the agricultural processing
industry. Indeed, lignocellulose, in the form of oat hulls, corn stover, wheat straw, and
similar materials, are usually considered as wastes (Wyman 1996). However, it has
long been recognized that cellulose can be converted to sugars followed by fermentation
to alcohol or organic acids. If agricultural wastes such as corn stover could be
economically converted to industrial chemicals, it would represent an important new
source of income for farmers or profit centers for agricultural businesses.
The biomass feedstocks typically contain from 55 to 75 per cent (by dry
weight) carbohydrates, typically polymers of five- and six-carbon sugar unit (Wyman,
2003). Most of all these carbohydrates can be converted to maximize ethanol
production.
Structure of Biomass
Cellulose
Cellulose, a higher molecular weight linear polymer, is composed of D-glucose
building blocks, joined by ?-1,4-glucosidic bonds (Tengborg et al., 1998). In native
cellulose, each cellulose molecule is a long unbranched chain of D-glucose subunits with
12
a molecular weight ranging from 50,000 to over 1 million (James and David, 1986).
These molecules, along with hemicellulose and lignin, are aggregated into long bundles
called microfibrils. Hydrogen bonding binds the cellulose molecules. As a result,
these fibers are composed of a crystalline or highly-ordered region that protects the
microfibrils from hydrolytic degradation and a less ordered, amorphous region (Fan et al.,
1983). The amorphous component is digested more easily by enzymatic attack than the
crystalline component. This results in a difference in reactivity and adsorption that may
result from variation in crystal structure, accessibility to the enzyme, and the degree of
polymerization (Gould, 1984; Shiang, 1985)
Figure II-2. A simplified model to illustrate the cross-linking of cellulose micro fibrils
and hemicellulose in the lignocellulosic biomass. Source: Hopkins, W.G., Introduction to
Plant Physiology, 2
nd
edition. John Wiley & Sons, Inc., New York.
13
The cellulose fibrils are clustered in microfibrils, which are often depicted in
cross section as in Figure II-2. Here the solid lines denote the planes of the glucose
building blocks, and the broken lines represent orientation of another important group of
polysaccharides called hemicellulose.
Hemicellulose
Hemicellulose is a shorter chain, amorphous polysaccharide of cellulans and
polyuronides (Wyman, 1996). Cellulans are heteropolymers made up of hexosans
(mannan, galactan, and glucan) and pentosans (xylan and arabinan). Polyuronides are
similar to cellulans, but contain appreciable quantities of hexuronic acids as well as some
methoxyl, acetyl, and free carboxylic groups. Xylan and glucomannan are dominant
carbohydrate components of hemicellulose (Chum et al., 1985).
Lignin
Lignin is composed of polymerized phenylpropanoic acids in a complex three-
dimensional structure. Ether and carbon-carbon bonds hold individual monomers
together (Chang et al., 1981; James and David, 1986). Lignin is formed by a free
radical polymerization mechanism and has a random structure. Lignin and
hemicellulose are believed to form an effective sheath around cellulose fibers, which
adds structural strength to the biomass matrix. Covalent bonds may exist between
hemicellulose and lignin, but this is uncertain. The amount of lignin in herbaceous
species and agricultural residues is approximately 10 to 20 per cent (Ooshima et al.,
1990; Schell et al., 1998).
14
Solka Floc
Highly purity samples of cellulose and its derivatives have been produced from
various kinds of naturally occurring celluloses. The transformation of the naturally-
occurring cellulose into pure cellulose or cello-oligomers involves complicated processes
such as the removal of hemicellulose and lignin, acetylation of hydroxyl groups, acidic
cleavage of the cellulose backbone, and fractionation by chromatography (Kobayashi et
al., 1999).
Solka Floc, a purified cellulose, is made from wood pulp through a cellulose
transformation process. The KS1016 and the BW300 grades of Solka Floc have an
average particle (fiber) length of 290 ?m and 22 ?m, respectively. The BW300 grade
has a degree of crystallinity of 62 to 65 per cent whereas the KSI016 grade has a greater
proportion of crystalline cellulose at 75 to 77 per cent (Murray, 1993).
PRETREAMENT IMPORTANCE
The susceptibility of cellulose as a substrate for enzymatic conversion processes
is determined by its accessibility to extracellular enzymes or other reactants secreted by
cellulosic microorganisms (McMillan, 1994). The structure of lignocellulosics in the
cell wall resembles that of a concrete pillar with cellulose fibers being the metal rods and
lignin the natural cement. Biodegradation of untreated native lignocellulosics is slow,
giving a very low extent of degradation (Chang et al., 1981; Kaar et al., 1998). To
increase the susceptibility of cellulosic material, structural modification by means of
various pretreatment strategies is indispensable. The resistance of biomass to enzymatic
attack can be contributed to three major factors (James and David, 1986):
15
? Cellulose in lignocellulosic biomass has highly resistance crystalline structure.
? Lignin surrounding cellulose forms a physical barrier.
? Sites available for enzymatic attack are limited.
Cellulose in lignocellulosics is composed of crystalline and amorphous
components. The amorphous component is digested more easily by enzymatic attack
than the crystalline component. The presence of lignin forms a physical barrier for
enzymatic attack, and hence, pretreatment causing disruption of the lignin linkage,
increases the accessibility of cellulose and eventually its hydrolysis rate.
Thus, pretreatment is as essential prerequisite to enhance the susceptibility of
lignocellulosic residues to enzyme action. An ideal pretreatment would accomplish
reduction in crystallinity, concomitant with a reduction in lignin content and increase in
surface area. Of various pretreatment methods, chemical pretreatments have been used
extensively to remove lignin and for structural modification of lignocellulosics.
Although various forms of chemical pretreatment of cellulosic materials have
been proposed, their effectiveness varies, depending on the substrate. Hence optimal
pretreatment must be established for each substrate.
PRETREATMENT TECHNIQUES
Depending on the material, most pretreatment techniques require preparation of
the starting substrate, normally involving a mechanical size reduction step with the
substrates sized appropriately for handling (Chum, 1985; Um, 2002). With this
definition, pretreatment can be broadly classified into three methods: physical,
chemical, or biological, Depending on the principal mode of action on the substrate
16
(Chang et al., 1981). The various pretreatment methods that can enhance the cellulose
digestibility are summarized in Table II-2 (James and David, 1986). Some processes
are combinations of two or more pretreatment techniques applied in parallel or in series.
Although various forms of pretreatment of cellulosic material have been proposed, their
effectiveness varies with the substrate characteristics. Thus, an optimal method of
pretreatment must be established for each substrate.
Table II-2. Methods used for Pretreatment of Lignocellulosics.
Physical Chemical Biological
Ball-milling Alkali Fungi
Two-roll milling Sodium hydroxide
Hammer milling Ammonia
Colloid milling Ammonium sulfite
Vibro energy milling Acid
High pressure steaming Sulfuric acid
Extrusion Hydrochloric acid
Pyrolysis Phosphoric acid
High energy radiation Gas
Chlorine dioxide
Nitrogen dioxide
Sulfur dioxide
Oxidizing agents
Hydrogen peroxide
Ozone
Cellulose solvents
Cadoxen
CMCS
Solvent extraction of lignin
Ethanol-water extraction
Benzene-ethanol extraction
Ethylene glycol extraction
Butanol-water extraction
Swelling agents
(James and David, 1986).
17
ENZYMATIC HYDROLYSIS
Enzymatic hydrolysis of cellulose is carried out by cellulase enzymes that are
highly specific (B?guin and Aubert, 1994). The products of the hydrolysis are usually
reducing sugars, including glucose. Utility cost of enzymatic hydrolysis is low
compared to acid or alkaline hydrolysis, is usually conducted at mild conditions (pH 4.8
and temperature 45 to 50
o
C) and does not have a corrosion problem (Duff and Murray,
1996).
Cellulases are usually a mixture of several enzymes. Three major types of
cellulolytic activity produced by fungi are (Gould, 1984):
? Endoglucanase (1, 4-?-D-glucan 4-glucanohydrolase)
? Cellobiohydroliase (1, 4-?-D-glucan cellobiohydrolase)
? ?-Glucosidase (?-D-glucoside glucohydrolase)
These enzymes have been purified or partially purified from fungi and their properties
studied. In spite of differences reported in substrate specificity, the general view can be
summarized as follows: (Shiang 1985; Um 2002):
Endoglucanase randomly hydrolyzes ?-1, 4-glycosidic linkages. It does not attack
cellobiose but hydrolyzes cellodextrins, acid-swollen cellulose and substituted celluloses,
such as CMC (carboxymethyl cellulose) and HEC (hydroxyethyl cellulose). It is also
claimed that some endoglucanases act on crystalline cellulose. The specificity of this
enzyme cannot be high since it readily attacks highly substituted celluloses.
Cellobiohydrolase acts on cellulose, splitting-off cellobiose units from the non-reducing
end of the chain. This enzyme does not attack substituted celluloses, reflecting higher
substrate specificity than that of endoglucanase. Cellobiohydrolase hydrolyzes
18
cellodextrins but not cellobiose. ?-Glucosidase hydrolyzes cellobiose and cello-
oligosaccharides to glucose. The enzyme does not attack cellulose or higher
cellodextrins.
In addition to these groups of cellulase enzymes, a number of ancillary
enzymes, such as glucuronidase, acetylesterase, xylanase, ?-xylosidase,
galactomannanase and glucomannanase, attack hemicellulose (Duff and Murray, 1996).
During the enzymatic hydrolysis, cellulose is degraded by the cellulases to reducing
sugars that can be fermented by yeasts or bacteria to ethanol.
Cellulase activity is inhibited by cellobiose and, to a lesser extent, by glucose.
Several methods have been developed to reduce the inhibition, including the use of high
concentrations of enzymes, the supplementation of ?-glucosidase during hydrolysis, and
the removal of sugars during hydrolysis by ultrafiltration or simultaneous saccharification
and fermentation (SSF). The SSF process has been extensively studied to reduce the
inhibition by end products of hydrolysis (Takagi et al., 1977; Blotkamp et al., 1978;
Szczodark and Targonski, 1989; Saxena et al., 1992; Philippidis et al., 1993; Zheng et al.,
1998; Varga, 2004). In the process, reducing sugars produced in cellulose hydrolysis or
saccharifications are simultaneously fermented to ethanol, greatly reducing the end
product inhibition to the hydrolysis.
ETHANOL FERMENTATION
Fermentation, one of the oldest chemical processes known to man, is used to
make a variety of products, including foods, flavorings, beverages, pharmaceuticals, and
chemicals (Fan et al., 1987). At present, however, many of the simpler products, such as
19
ethanol, are synthesized from petroleum feedstocks at lower costs. The future of the
fermentation industry therefore depends on its ability to utilize the high efficiency and
specificity of enzyme catalysis to synthesize complex products and on its ability to
overcome variations in quality and availability of raw materials (NREL, 2001).
Ethanol is made from a variety of agricultural products such as grain, molasses,
fruit, whey and sulfite waste liquor. Generally, most of the agricultural products
mentioned above command higher prices as foods, and others, like potatoes, are
uneconomical because of their low ethanol yield and high transportation cost. The
energy crisis of the early seventies may have generated renewed interest in ethanol
fermentation, but ethanol?s use still depends on the availability and cost of the
carbohydrate relative to the availability and cost of ethylene (Capital Press, 2005).
Sugar and grain prices, like oil prices, have risen dramatically since 1973.
Fermentation processes from any material that contains sugar can produce ethanol.
The many and varied raw materials used in the manufacture of ethanol via fermentation
are conveniently classified under three types of agricultural raw materials: sugar, starches,
and cellulose materials (Wyman, 1994). Sugars (from sugar cane, sugar beets, molasses,
and fruits) can be converted to ethanol directly. Starches (from grains, potatoes, root
crops) must first be hydrolyzed to fermentable sugars by the action of enzymes from malt
or molds. Cellulose from wood, agricultural residues and waste sulfite liquor from pulp
and paper mills must likewise be converted to sugars, generally by the action of mineral
acids. Once simple sugars are formed, enzymes from yeast can readily ferment them to
ethanol.
20
BIOETHANOL FERMENTATION PROCESS
The hydrolysis of cellulose yields glucose and xylose. Once these sugars are
available, the fermentation of carbon source is no longer a difficult task, as this
technology is well-developed. Essentially four types of processes that can convert
cellulose to ethanol: direct microbial conversion (DMC), separate hydrolysis and
fermentation (SHF), simultaneous saccharification and cofermentation (SSCF), and
simultaneous saccharification and fermentation (SSF). Extensive research has shown
that among the various cellulose bioconversion schemes, SSF seems to be the most
promising approach to biochemically convert cellulose to ethanol in an effective way
(Takagi et al., 1977; Wright et al., 1988; Varga, et al., 2004).
Direct Microbial Conversion (DMC)
DMC uses one microorganism such as Fusarium sp., Neurospora crassa or
Clostridium sp. for both cofermentation and enzyme production. To date this process has
not been successful due to its complexity. The current DMC strains are typically
characterized by slow growth rates. A low ethanol yield and productivity can be
achieved in DMC with low ethanol selectivity; therefore the strains used produce other
fermentation products (e. g., acetic acid) at nearly equivalent levels as ethanol
(Christakopoulos et al., 1990; Padukone, 1996; Himmel et al., 1997). However, South
et al. (1993) have reported a promising conversion of substrate (pretreated hardwood
flour) to ethanol (77per cent) in the DMC system with Clostridium thermocellum using a
well-mixed stirred-tank reactor (CSTR). Stevenson et al. (1999) have made genetic
21
engineering attempts to develop a microorganism starting from Clostridium
thermocellum for DMC.
Separate Hydrolysis and Fermentation (SHF)
SHF is a conventional two-step process where cellulose is enzymatically
hydrolyzed by cellulase to form glucose in the first step and glucose is fermented to
ethanol in the second step by using Saccharomyces or Zymomonas (Bisaria and Ghose,
1981; Johnson et al., 1982; Hogsett et al., 1992; Philipidis, 1996, Lawford et al., 1999).
The main advantage of SHF is the ability to carry out each step at its optimum
temperature, 45 to 50
o
C and 30
o
C, respectively. The major disadvantage results from
the fact that enzymatic hydrolysis severely inhibits cellulase activity during hydrolysis.
Hence, lower cellulose concentration and higher enzyme loading must be used to obtain
reasonable ethanol yields
Simultaneous Saccharification and Cofermentation (SSCF)
The fermentation of hexoses and pentoses is carried out simultaneously with a
cofermenting microorganism such as Zymomonas mobilis (Himmel et al., 1997; Lawford
et al., 1999; Glazer and Nikaido, 1995; Takagi et al., 1978). When the lignocellulosic
substrate has a high content of pentoses, e. g. xylose, a separate pentose fermentation step
is required to convert the substrate to ethanol economically, as the currently used
fermentative microorganism in SSF does not convert pentoses.
The SSCF process option offers the possibility of substantial capital and
operational savings by reducing the number of required reactors and saving the cost of
22
steam separation. Based on preliminary economic analysis, the cofermentation indicates
a potential 18 per cent reduction in the ethanol cost compared to the basic design
(Padukone, 1996).
Simultaneous Saccharification and Fermentation (SSF)
SSF is the most promising process for ethanol production from lignocellulosic
materials with multiple researchers focusing on the process (Lawford et al., 1999; Klinke
et al., 2001; ?hgren et al., 2005; Philippidis and Hatzis, 1997; Stenberg, 2000; Teymouri,
2005 ;Varga et al., 2004;). In SSF, the enzymatic hydrolysis and fermentation steps are
performed simultaneously with only low levels of cellulobiose and glucose observed in
the reactor. Cellulase inhibition is reduced thus increasing sugar production rates,
concentrations, and yields and decreasing enzyme loading requirements. The
drawbacks associated with this process include the different operating conditions required
for enzymatic hydrolysis and fermentation and the problem of yeast and bacteria
recycling. Krishnan et al. (1997) have reported a fed-batch SSF process for ethanol
production from pretreated corn fiber that achieves higher ethanol productivity at reduced
enzyme loadings compared to the batch SSF process. Padukone et al. (1996) have
demonstrated that, in the late stage of SSF with near starvation of the yeast, the addition
of glucose shifted the metabolic pathway to ethanol production from succinic and acetic
pathways. Varga et al. (2004) have conducted SSF process with high initial pretreated
corn stover at flask scale.
23
Theoretical Ethanol Yield via Fermentation Process
The amount of product formed per unit of substrate consumed by the organism is
a useful way to refer to yields. Yields are expressed on either molar or weight basis.
For process cost accounting purpose, weight is more meaningful.
In this case the primary stoichiometric equations for the ethanol production are
as follows (Hettenhaus, J. R. 1998):
Pentosan to Pentose (13.6 weight per cent gain)
n C
5
H
8
O
4
+ n H
2
O ? n C
5
H
10
O
5
n 132
MWU
n 18
MWU
n 150
MWU
(1gram) (0.136 g) (1.136 g)
Hexosan to Hexose (11.1 per cent weight gain)
n C
6
H
10
O
5
+ n H
2
O ? n C
6
H
12
O
6
n 162
MWU
n 18
MWU
n 180
MWU
(1gram) (0.1111 g) (1.111 g)
Pentose and Hexose to Ethanol (48.9 per cent weight loss)
Pentose: 3 C
5
H
10
O
5
? 5 C
2
H
5
OH
+ 5 CO
2
3?150
MWU
5?46
MWU
5?44
MWU
(1gram) (0.511 g) (0.489 g)
Hexose: C
6
H
12
O
6
? 2 C
2
H
5
OH
+ 2 CO
2
180
MWU
2?46
MWU
2?44
MWU
(1gram) (0.511 g) (0.489 g)
24
A reduction in yield below theoretical values always occurs since the microorganism
requires a portion of the substrate for cell growth and maintenance. The fermentation
process takes around three days to complete and is carried out at a temperature of
between 25 and 30
o
C.
BIOREACTOR DESIGN
For high-solids fermentations an effective bioreactor should achieve good enzyme,
substrate, microorganism distribution and uniform temperature control with low mixing
power input. Production-scale bioreactors can be developed by modeling laboratory
reactor performance and by scaling up a process design that minimizes ethanol cost and
maximizes SSF?s productivity. Batch experimental studies have helped identify
enzymatic hydrolysis as the limiting step in the SSF and the need to enhance substrate
accessibility by improving the effectiveness of biomass pretreatment.
SSF studies have provided information on crucial operating parameters,
performance variables, and scale-up considerations, such as dilution rate and ethanol
yield and predictability, as the process moves toward commercialization. Cellulose
conversion factors of major importance, such as the effect of substrate and enzyme
loading on ethanol predictability, the most efficient mode of operation, the effect of
feedstock composition, and the desired pretreatment effectiveness can be systematically
evaluated to improve the overall biomass-to-ethanol technology.
CFD provides for the numerical modeling of the flow field imposed by the
impeller and sparging system (including shear rates) and a prediction of the mixing of
chemical species with the vessel. The development of cell culture and fermentation
25
processes using CFD significantly reduces the amount of experimentation required,
provides more data than physical trials, and allows for the evaluation of new equipment
prior to purchase.
Mixing in Bioreactors
Mixing effects in chemical reactors are usually separated into two components:
macromixing and micromixing. In many cases for simple homogeneous reactions
micromixing has a limited effect on conversion. However, this effect usually cannot be
neglected in the design of heterogeneous reactors or in homogeneous reactors where
complex or autocatalytic reactions take place. The importance of mixing was
summarized by Cooke et al. (1988) as follows:
? Poor mixing leads to undesirable nutrient concentration gradients and can lead to
nutrient starvation in stagnation regions of the vessel. For aerobic yeast
production oxygen starvation leads to ethanol production which adversely effects
productivity.
? Good mixing is important for heat transfer. Fermentation equipment needs to be
designed to provide adequate fluid velocities adjacent to heat transfer surface.
? When mixing constants approach those of the time constant for mass transfer, the
commonly-used well mixed liquid phase assumption for the determination and
scale-up of mass transfer is no longer appropriate. Obviously, if the mixing is
not considerably faster than the mass transfer, then the chance of undesirable
oxygen and other nutrient gradients are enhanced. These effects require
quantification for scale-up.
26
Reactor Geometries
Figure II-3 illustrates the nomenclature used to describe the mixing system
(Oldshue, 1983).
Figure II-3. The standard geometry of mixing tank; nomenclature used to describe the
mixing system (Oldshue, 1983).
Impeller off-bottom distance C is measured form the lower impeller?s horizontal
centerline to the lowest point on the tank bottom. Flat bottoms, shallow cones, and
standard ASME dishes (depth=
6
1
T) are treated in the same manner. Coverage CV is
measure from the static liquid surface to the horizontal centerline of the upper or lower
(single) impeller. Spacing between impellers S is measured from one impeller
horizontal centerline to the next. Liquid depth Z is measured form the static liquid
surface to the lowest point on the tank bottom.
27
Turbine Agitators
A wide variety of turbine-style impellers are available. Figure II-4 lists the
most common ones: the pitched-blade turbine, high efficiency impeller, disc turbine,
and simple straight-blade turbine. Well designed turbine impeller systems can be used
up to viscosities of about 50 Pa-s, depending on the scale, application, and process
requirements (Bakker, 1995).
Figure II-4. A wide variety of turbine is available to handle viscosities to about 50 Pa-s.
Each impeller has its own characteristics and its own area of application.
Straight-blade impellers and disc turbines tend to be used to create zones with high shear
rates, such as in dispersion. High-efficiency and pitched-blade turbines tend to be used
at Reynolds numbers larger than about 100 where a good overall circulation flow is
important. The impeller Reynolds number is defined as:
eff
ND
N
?
?
2
Re
= (1)
28
For Newtonian fluids, the effective viscosity ?
eff
is simply the dynamic
viscosity. For non-Newtonian fluids, the viscosity will vary with shear rate throughout
the volume of the tank. To calculate the average viscosity experienced by the impeller,
an effective shear rate S
eff
is calculated by Metzner and Otto (1957):
KNS
eff
= (2)
Here, N is the impeller rotational speed in reciprocal seconds and K is an
impeller constant. Table II-3 lists K-values for several different impeller types.
Substituting the effective shear rate in the viscosity equation for the particular fluid then
gives the effective viscosity that can be used to calculate the impeller Reynolds number.
Table II-3. K-Values for Effective Shear-Rate Model
Impeller Type K
High Efficiency 10
Pitched Blade 11
Straight Blade 11
Disc Turbine 11.5
Anchor (D/T=0.98) 24.7
Helical Ribbon (D/T=0.96, P/D=1) 29.4
The values for the anchor and helical ribbon impellers are for the specified standard geometries only.
Several other methods of calculating the effective shear rate have been reported in the
literature. Most of these are more complicated than the method used here, while not
always more accurate.
Two other dimensionless numbers that are often used to characterize the impeller
are the power number and the pumping number. The power number N
p
is implicitly
defined by the following equation:
29
53
DNNP
p
?= (3)
When the power number of an impeller is known, the power can be calculated
using this equation, given liquid density, impeller speed, and diameter.
The pumping number N
q
is implicitly defined by:
3
NDNQ
ql
= (4)
When the pumping number of an impeller is known, the pumping rate of the
impeller Q
l
can be calculated for a given diameter and speed. Both the impeller power
number and pumping number depend on a variety of factors, such as the ratio of impeller
to tank diameter D/T, the impeller off-bottom clearance ratio C/T, and the impeller
Reynolds number.
For Reynolds number above 10,000, both N
p
and N
q
show little variation with
Reynolds number. In this regime, the flow is considered to be fully turbulent. When
the Reynolds number decreases, the pumping number decreases, while the power number
increases. This regime is usually called the transitional regime.
At very low Reynolds numbers (N
Re
<10), the power number is inversely
proportional to the Reynolds number. In this Reynolds number regime, the flow is
considered laminar. The exact Reynolds numbers at which the transitions occur are a
function of impeller type, size and number (Grenville et al, 1995). The correlation for
the Reynolds number at the boundary between the turbulent and transitional regime is as
follows
N p
N
3
1
Re
370,6
= (5)
30
Helical-Ribbon and Anchor Impellers
An alternative to selecting a turbine impeller system is the use of a helical-ribbon
impeller or an anchor impeller (Figure II-5). These impellers sweep the whole wall
surface of the tank and physically agitate most of the fluid batch. As a result, they can be
used at much lower Reynolds numbers (N
RE
?400) than the open-style turbines.
Figure II-5. Helical-Ribbon and Anchor Impellers Provide an Alternative to Turbine
Impeller.
Helical ribbons are primarily used when very viscous materials are to be mixed
starting at viscosities of about 20 Pa-s and higher, depending on the scale. Helical
ribbons have been successfully used for viscosities up to 25,000 Pa-s (Bakker, 1995).
31
These impellers often have a diameter that is close to the inside diameter of the tank and
therefore, are sometimes called close clearance agitators. They guarantee liquid motion
all the way to the wall of the tank, even for viscous materials.
Helical impellers can also be manufactured in a different configuration, with the
helical impeller blade wrapped closely around the shaft (Figure II-5 b). Such impellers
are also called helical screw impellers and are sometimes used in combination with draft
tube.
When these screw impellers are combined with a close-clearance helical ribbon as
shown in Figure II-5 c, the screw is sometimes called an auger. The power draw from
an impeller is proportional to the diameter to the 5
th
power, as in equation (3). Since the
diameter of the auger is much smaller than the diameter of the outside helical ribbon, the
power draw of an auger is often less than 1 per cent of that of a helical ribbon. When
correctly designed, an auger can be used to improve the mixing near the shaft without
significantly increasing the power draw and torque requirement of the agitator. If the
auger is incorrectly designed and the pumping capacity of the auger does not match the
flow created by the helical ribbon impeller, the auger may actually block the liquid flow
near the shaft and hamper the mixing process.
When it is important to have high velocities at the tank wall, for example, in heat-
transfer applications, it is recommended to make the clearance between the helix and the
tank wall as small as possible. Further, the helix can be equipped with scrappers that
physically remove the fluid from the tank wall.
32
Conventional Radial Flow Turbine
The flat-blade or Rushton turbine (Figure II-4c) is the historic standard for gas-
liquid applications and is widely employed throughout the process industries. Given its
simple construction, it can be easily 'tuned' to an application by adding or subtracting
blades, or by adjusting the radial position of the blades to change turbine diameter.
While the impeller features high pumping capacity, its radial discharge is not efficient for
solids suspension. Similarly, it is capable of dissipating a large amount of energy,
although this is done at the expense of high shear input. The nature of the Rushton
turbine blade shape and the vortex that forms behind it leads to a marked decrease in
power draw as gas is introduced. Two-speed motors can be used to optimize the drive
system for both gassed and ungassed operation.
Given the variety of gas handling impellers available, there are typically better-
suited options that can be employed to achieve desired process results. Applications
requiring high shear or high-energy dissipation rates remain the primary use for flat-blade
turbines.
Flow Patterns
When an impeller is used in a tank which has a high ratio of its height to diameter,
a draft tube can ensure good top-to-bottom mixing (Oldshue, 1983). This circular duct
which is used to direct fluid flow to and from the impeller is most commonly used with
axial-flow devices, which are inserted into the tube.
The distribution of flow in an open-impeller system is shown in Figure II-6 and
Figure II-7. When the ratio of liquid depth to the tank diameter is greater than 1.0,
33
uniform solids suspension is not readily obtained for fast settling solids. A strong flow
pattern produced by down-pumping draft tube circulators sweeps the solids away form
the draft tube and reduces the tendency for solids deposition.
Figure II-6. Streamline Pattern in a Standard Cylindrical System with Axial high-speed
Impeller and Radial Baffles (Oldshue, 1983, from Fort et al.).
Figure II-7. Streamline Patterns in Cylindrical System with Axial High-Speed Impeller
and Draft Tube (Solid lines are calculated streamlines; dashed lines are anticipated course
of streamlines) (Oldshue, 1983, from Fort et al.).
34
Examination of flow and power numbers for both open tank and draft tube using
the same impeller indicates that a specified primary circulation rate can be achieved more
efficiently with the draft tube unit (Oldshue, 1983). Since the draft tube serves to
improve (i.e., make more uniform) the inlet fluid velocity profile, it markedly reduces the
variation in load experienced by the impeller. Thus, the fluid forces acting on an
impeller in a draft tube are markedly reduced. Based on limited testing, the force is
observed to be less than 20 per cent of that observed in similar open tank service.
Scale Up Method for Fermentors
Agitation and aeration directly influence transport phenomena in the fermentor;
therefore, the study of scale-up is based on the agitation and aeration of the fermentor
(Maxon and Johnson, 1953). To solve the scale-up problems, the principal
environmental parameters affected by aeration and agitation (oxygen concentration, shear,
bulk mixing) are identified and studied. The process variables (airflow rate and
agitation speed) are calculated for the large-scale fermentor to give the same
environmental parameters as in the small-scale fermentor.
Ju and Chase (1992) suggest that the following environmental parameters should
be kept constant during scale-up:
? reactor geometry
? volumetric oxygen transfer coefficient
? maximum shear rate
? power input per unit volume of liquid
? volumetric gas flow rate per unit volume of liquid
35
? superficial gas velocity
? mixing time
? impeller Reynolds number
? momentum factor
The standard geometry is assumed to be the optimum geometry for reactors.
The standard geometry is shown in Figure II-3 (Oldshue, 1983). All scale up studies of
fermentors are developed experimentally using geometrically similar reactors of different
sizes. Instead of using geometrically similar reactors, the constant mixing time is used
in a viscous non-Newtonian system. This criterion is unnecessary for normal
fermentations and requires more power. Impeller Reynolds Number and momentum
factor criteria are not considered in calculating the effect of aeration on the process.
The most important problem in aerobic fermentations is oxygen transfer. To
maintain the oxygen transfer rate as vessel size increases, scale-up of aerobic
fermentations is typically done with a constant volumetric oxygen transfer coefficient
(Karow et al, 1953; Andrew, 1982). The criterion to keep power input per unit volume
of liquid and maximum shear constant is important when growing shear-sensitive
microorganisms. The volumetric gas flow rate per unit volume of liquid (Q) criterion
and the superficial gas velocity (V
s
) criterion are contradictory to each other when
applied to geometrically similar fermentors. If the Q is maintained constant, the
superficial gas velocity will increase directly with the scale ration. The choice between
the volumetric gas flow rate per unit volume of liquid criterion and the superficial gas
velocity criterion has to be carefully considered for the process.
36
Scale up rules generally used in the fermentation industries are constant power
input per unit volume of liquid (P/V), constant k
L
a, constant impeller tip speed (?
tip
), and
constant dissolved oxygen concentration (Mavituna, 1996). Frequency of use of the
various rules is given in Table II-4. For scale up of aerobic fermentation, the oxygen
transfer rate is the important factor (Hubbard, 1987) and can be calculated as follows:
For the plant-scale fermentor:
? choose the required volume
? calculate the dimensions based on geometry similarity
? establish the scale-up strategy to be used (k
L
a)
plant
= (k
L
a)
lab
? calculate air flow rate (V
air
) and agitation rate (N) with either method 1 or 2
? estimate power consumption
Method 1:
? determine V
air
using Q
? calculate N from power and k
L
a correlation
Method 2:
? determine N using ?ND constant (?
tip
constant)
? calculate V
air
from power and k
L
a correlation
Table II-4. Scale-up criteria in fermentation industries.
Scale-Up Criterion Used Percentage of Industries
Constant P/V 30
Constant k
L
a 30
Constant ?
tip
20
Constant pO
2
20
F. Mavituna, Strategies for bioreactor scale-up, In: Computer and Information Science
Application in Bioprocess Engineering (A. R. Moreira and K.K. Wallace), Kluwer
Academic Publishers, Dordrecht, Netherlands, 1996, pp. 129.
37
The accuracy of this procedure depends on the relationship between k
L
a and
power consumption. This procedure is used widely in scale-up aerobic fermentation.
However, poor mixing and hidden auxotroph are two factors still uncertain on scale-up
(Humphrey, 1998). Success in scale-up of fermentation requires the preliminary
calculation of environmental parameters and then trial and error testing to achieve the
same results as in the laboratory scale.
Simulation Using Computational Fluid Dynamics
Computational fluid dynamics has evolved over the forty years of its existence
from the specialty of a small, closely knitted band of enthusiasts into a vast and many-
sided enterprise. The business of CFD applications alone now employs several tens of
thousands of people and has a turnover of some billions of dollars a year. Moreover, the
material resource committed to CFD research and developments are still increasing
rapidly, as existing areas of application continue to expand and new areas are constantly
being opened up.
The automatic digital computer, invented by Atanasoff in the late 1930?s, was
used from nearly the beginning to solve problems in fluid dynamics (Anderson et al.,
1984). The development of the high-speed digital computer has had a great impact on
the way in which CFD principles from the science of fluid mechanics are applied to
problems of design in modern engineering practices.
The application of computational fluid dynamics can be fond in (FLUENT
INC., 2001)
38
? Process and process equipment applications
? Power generation and oil/gas and environmental applications
? Aerospace and turbo machinery application
? Automobile applications
? Heat exchanger applications
? Electronics/HVAC/appliances
? Materials processing applications
? Architectural design and fire research
In this approach, the equations (usually in partial differential form) that govern a process
of interest are solved numerically.
The basic equations describing the laminar flow of continuous fluid are (FLUENT
INC., 2001):
conservation of mass:
mi
i
Su
xt
=
?
?
+
?
?
)(?
?
conservation of momentum:
ii
j
ij
i
ji
j
i
i
Fg
xx
p
uu
x
u
x
++
?
?
+
?
?
?=
?
?
+
?
?
?
?
?? )()(
where the stress ?
ij
is given by:
ij
i
i
i
j
j
i
ij
x
u
x
u
x
u
????
?
?
?
?
?
?
?
?
?
?
?
?
?
+
?
?
=
3
2
)(
conservation energy:
h
j
i
ij
i
ijj
iii
i
i
S
x
u
x
p
u
t
p
Jh
xx
T
k
x
p
hu
x
h
t
+
?
?
+
?
?
+
?
?
+?
?
?
?
?
?
?
?
=
?
?
+
?
?
??? )()()(
h is defined as
?
=
i
ii
hmh and
?
=
T
T
pii
ref
dTCh
39
conservation of chemical species:
''''
),()()(
iii
i
ii
i
i
SJ
x
mu
x
m
t
+
?
?
=
?
?
+
?
?
??
where
i
i
iii
x
m
mDJ
?
?
?=
'
'''
,, ?
The equations are reduced to their finite difference algebraic equations by
integration over the computational cells into which the domain is divided. After
integration of the fluid motion governing equations, the resulting algebraic equations can
be written in the following common form:
??
+=?
i
cii
i
pip
SASA )()( ??
Where the summation is over the neighboring finite difference cells i=N, S,E,W,F,B
( which stand for North, South, East, West, Front, and Back). The A?s are coefficients
which contain contributions from the convective and diffusive fluxes, and S
c
and S
p
are
the components of the linearized source term,
ppc
SSS ?
?
+= . A power law
differencing scheme is used for interpolation between grid points and to calculate the
derivatives of the flow variables. The set of simultaneously algebraic equations is
solved by a semi-implicit iterative scheme which starts from arbitrary initial conditions
and converges to the correct solution after performing a number of iterations.
Each iteration consists of the steps which are outlined below (FLUENT INC.,
2001).
? The u, v and w momentum equations are each solved in turn using current values
for pressure, in order to update the velocity field.
? Since the velocities obtained in the above step may not satisfy the mass continuity
equation locally, a ?Poisson-type? equation is derived from the continuity
40
equation and linearized, momentum equations. This ?pressure correction?
equation is then solved to obtain the necessary corrections to the pressure and
velocity fields such that continuity is achieved.
? The k and ? equations are solved using the updated velocity field (for turbulent
flow only)
? Any auxiliary equation (e.g. enthalpy, species conservation, or any additional
turbulence quantities) are solved using the previously updated values of the other
variables
? The fluid properties are updated
? A check for convergence of the equation set is made
These steps are continued until the error has decreased to a required value.
The accuracy of a computational solution to a partial differential equation can be
affected by two types of source errors (Souvaliotis et al., 1995)
? Truncation error
? Round-off err or
The limiting behavior of the truncation error can be characterized by employing a
Taylor series expansion for ),(
00
yxxu ?+ about (x
0
, y
0
)
........
!2
)(
))),(),(
2
0
2
2
00000
+
?
?
?
+?
?
?
+=?+
x
x
u
x
x
u
yxuyxxu
In terms of the finite difference representation for
x
u
?
?
+
?
?
=
?
?
+
x
uu
x
u
ji
j
i ,,1
truncation error
41
The round-off errors are generated by rounding floating point numbers to a finite
number of digits in the arithmetic operations in obtaining machine solutions to finite
difference equations because of the large number of dependent, repetitive
operations which are usually involved. Anderson et al. (1984) state that, in some type of
calculations, the magnitude of the round-off error is proportional to the number of grid
points in the problem domain. In these cases refining the grid may decreased the
truncation error but increase the round-off error.
Anderson et al. (1984) propose that, in order for the computational solution to be
acceptable, the finite difference representation of the partial differential equation needs to
meet the conditions of consistency, stability, and convergence.
Consistency deals with the extent to which the finite difference equations
approximate the partial differential equations. A finite difference representation of
partial differential equation is consistent if it can be shown that the difference between
the partial differential equation and its difference representation vanishes as the mesh is
refined.
A stable computational scheme is one for which errors from any source (round-off,
truncation, mistakes) are not permitted to grow in the sequence of numerical procedure as
the calculation proceeds form on marching step to the next. Anderson et al. (1984)
observed that concern over stability occupies much more attention than concern over
consistency.
Convergence here means that the solution to the finite difference equation
approaches the true solution to the partial differential equation having the same initial and
boundary conditions as the mesh is refined. Lax?s equivalence theorem shows that,
42
given a properly posed initial value problem and a finite difference approximation that
satisfies the consistency condition, stability is the necessary and sufficient condition for
convergence.
Physical Model for Turbulence Fluid Motion
From the classical point of view, turbulence fluid motion is an irregular condition
of flow in which the various quantities (velocity, pressure, concentration, temperature,
etc.) show a random variation with time and space coordinates, but in such a way that
statistically distinct averages can be discerned (Hinze, 1989). It can also be defined as
an eddying motion with a wide spectrum of eddy size and a corresponding spectrum of
fluctuation frequencies. The motion is always rotational. The forms of the largest
eddies (low-frequency fluctuations) are usually determined by the boundary conditions,
while the forms of the smallest eddies (highest-frequency fluctuations) are determined by
the viscous forces (Rodi, 1980).
Basically, the physical turbulence models provide the solution the closure
problem in solving Navier-Stokes equations. While there are ten unknown variables
(mean pressure, three velocity components, and six Reynolds stress components), there
are only four equations (mass balance equation and three velocity component momentum
balance equations). This disparity in number between unknowns and equations make a
direct solution of any turbulent flow problem impossible in this formulation. The
fundamental problem of turbulence modeling is to relate the six Reynolds stress
components to the mean flow quantities and their gradients in some physically plausible
manner.
43
The time-averaged turbulence models (Table II-5) employ transport equations for
quantities characterizing the turbulence. So far, standard k-? model, RNG k-? model,
and RSM model have been implemented in the commercial CFD codes. These turbulent
models are different in terms of the number of transport equations used for turbulence
quantities.
It is worthwhile to note that the standard k-? model is usually employed with the
five coefficients as recommended by Launder and Spalding (1972). The five
coefficients are empirical, obtained based on simple experimental flow situation. Rodi
(1980) reported that solutions have been found to be sensitive to the five coefficients in
the standard k-? turbulence model. Although the standard k-? model with these five
coefficients has successfully simulated a number of real, fluid flow problems in two and
three dimensions, it is necessary to alter drastically the magnitude of the five coefficients,
so as to calibrate the model for a particular flow situation.
The RNG k-? model is touted by CFD code vendors as accurate, economic in
computer time and capable of prediction near wall transport phenomena without
limitations associated with the wall function approach. Streaklines showing flow
around a 2D bluff body are shown in Figure II-9. The RSM model may give better
simulation results, but this model may confront with the convergence problem and is very
demanding in terms of computational effort.
The RNG k-? model is touted by CFD code vendors as accurate, economic in
computer time and capable of prediction near wall transport phenomena without
limitations associated with the wall function approach. The RSM model may give better
44
Table II-5. Overview of Turbulence Models (FLUENT INC., 2001)
simulation results, but this model may confront with the convergence problem and is
demanding in terms of computational effort.
Turbulence
Model
Description, Advantage, and Disadvantages
k-?
The most widely used model. Its main advantages are short
computation time, stable calculations, and reasonable results for
many flows. Not recommended for highly swirling flows, round
jets, and in areas with strong flow separation.
RNG k-?
A modified version of the k-? model, with improved results for
swirling flows and flow separation. Not suited for round jets.
Not as stable as the standard k-? model.
k-?
Realizable
Another modified version of the k-? model. Solves the flow in
round jets correctly, and provides much improved results for
swirling flows and flows involving separation when compared to
the standard k-? model. More stable than the k-? RNG model.
RSM
The full Reynolds stress model provides good perditions for all
types of flows, including swirl, separation, and round and planar
jets. Longer calculation times than the k-?.
LES
Large Eddy Simulation provides excellent results for all flow
systems. LES solves the Navier-Stokes equations for large scale
motions of the flow and models only the small scale motions.
The main disadvantage is that the required computational
resources are considerably larger (often 10 to 100 times) than
with the RSM and k-? styles models, mainly because all
calculations are conducted in a time dependent fashion since
steady state flow is not assumed, and a finer grid is needed to
allow for accurate modeling of the turbulence at the subgrid
small scale level.
45
Figure II-8. Streaklines Showing Flow around a 2D Bluff Body (FLUENT, 2001).
Approaches for Mixing Tank Simulation
The CFD code users demand the approach for mixing tank simulation should have
the following capabilities:
? Independence from experimental data
? Capabilities to handle all impellers with complicated geometry
? Capability to model a non-symmetrically located impeller
? Capability to model a tank with complicated geometry
? Capability to model with the three types turbulence models
? Capability to model with a multiphase model
? Convergence in a stable and robust manner
However, none of the CFD code vendors has provided the module to meet the
above requirement from the users. Consideration effort has been contributed to
develop the source code to predict the fluid flow in a mixing tank with powerful
capabilities.
46
The LDA data approach has been used by a number of investigators. This
approach can handle the complicated geometry tank and non-centrally located impellers.
The limitations with LDA data approach is that LDA data are expensive to obtain, and
the simulation result is sensitive to the small error in specifying the boundary conditions
for the impeller region.
The rotating reference frame approach saves significant computational efforts
over full time-dependent simulations, but this approach poses a problem with
convergence because of the high degree of coupling between the momentum equations.
The sliding mesh approach, illustrated in Figure II-9, is touted by FLUENT to the
able to handle the impeller-baffle interaction without the need to simplify the problem.
The limitation with this approach is that RSM turbulence model and multiphase model
cannot be included.
Figure II-9. Sliding Grid Motion (FLUENT, 2001).
The rotating reference frame approach base on the unstructured mesh is quite
promising. This approach utilizes the powerful capabilities of unstructured mesh to
handle the complicated geometry. It could take a few years for this approach to be
released as a commercial CFD code.
47
Structured and Unstructured Mesh
Traditionally, CFD analysis of a design was based on the structure mesh. The
current move towards concurrent engineering requires that the analysis occur
concurrently with the development of the design. Integration of CFD analysis into this
process requires that the CFD analysis cycle time be significantly reduced. Currently,
one of the most time- consuming parts of performing a CFD analysis is the creation of the
geometry and an appropriate grid. One way to significantly reduce this is to provide
automatic unstructured mesh implementation. Also, unstructured mesh can be
employed to handle the complicated geometry, and the local mesh can be refined without
having to carry a fine mesh throughout the whole domain.
The drawback with the unstructured is that unstructured mesh module is not
compatible with the utility module used by structured mesh. This difference could
require considerable effort to develop the new utility code in the CFD vendor?s side and
the training and learning period in the user?s side.
Multiphase Simulation
Basically, the multiphase model is based on a number of assumptions to obtain
the necessary additional equations to close the partial differential equations. The
coefficients used in the assumption equations are obtained from empirical information.
The limitation is obvious. The application must possess similar physical phenomena.
The Eulerian multiphase model employed the concept of volume fractions, which
is quite pseudo one phase model. The coupling between each phase is achieved through
the pressure and interphase drag coefficients.
48
The Larangian multiphase model attempts to track a large number of dispersed
particles through the calculated continuous phase. The scarcity of the information about
mechanism of dispersed phase breakup and coalescence is one of the limitations for
multiphase models.
VISCOSITY OF CELLULOSIC SLURRIES
Rheology is defined as the science of deformation and flow properties of
materials. Rheological measurements provide critical information for product and
process performance and quality control and help reduce the cost and time needed for
development.
High solids saccharification and fermentation are difficult due to the challenging
rheological characteristics of high-solid biomass slurries that can cause non-uniform heat
and mass transfer. In addition, dynamic changes in rheology and biomass properties
occur as the cellulose structure is broken down during enzymatic hydrolysis.
Pimenova and Hanley (2003) estimated the viscosities of pretreated corn stover
slurries (average fiber length = 120 ?m) using a helical ribbon impeller viscometer.
Because the high-solids slurries were non-Newtonian, the viscosities varied with shear
rate in a power law relation, with order of magnitude increases starting at approximately
50 centipoise (Newtonian) at a level of 5 per cent solids (w/w, dry basis) and reaching
more than 10
6
centipoise (highly non-Newtonian) at a level of 30 per cent solids. The
viscosities of distiller's grain slurries were measured for solids concentrations of 21, 23,
and 25 per cent using a helical impeller viscometer over a range of rotational speeds.
The reported value of c (Newtonian impeller constant) was 151 and k (non-Newtonian
49
shear rate constant) was 10.30. The comparison was made to power law, Herschel-
Bulkley, and Casson viscosity models by regression analysis of experimental data with
regression coefficients exceeding 0.99 in all cases (Houchin and Hanley, 2004).
Non-Newtonian Behavior
Newtonian fluids can been described by Newton?s law of viscosity (Bird et al.,
2001):
?
?=
?
?
?= ????
y
V
x
yx
(6)
Newton?s equation for viscosity can be modified to characterize non-Newtonian behavior.
Some of the more widely used empirical models include the Bingham, Casson and power
law models. The two-parameter models (Bingham, power law, and Casson) have three
advantages. These methods can accurately fit data for a shear rate range, can usually
describe the rheology of filamentous suspensions, and are widely used in correlations of
transport properties of non-Newtonian systems with moderate success (Allen et al., 1990).
The Herschel?Bulkley model, proposed in 1926, describes the rheological characteristics
of filamentous suspensions.
The four model equations are frequently used to describing filamentous
suspensions:
Power law
n
K
?
= ??
(7)
Bingham
?
=? ????
p
B
y
(8)
Casson (1959)
5.05.05.0
)()(
?
+= ????
CC
y
(9)
50
Herschel-Bulkley (1926)
HB
n
HBHB
y
K
?
+= ???
(10)
For some thick suspensions and pastes no flow occurs until critical stress, the
yield stress is reached. The fluid flows in such a way that part of the stream are in plug
flow. The simplest model of a fluid with a yield value is the Bingham model. From
Equation 8 it follows that, if the shear stress is significantly larger than the yield stress,
Newton?s law applies (Bird et al., 2001).
The Casson and Bingham plastic models are similar because they both have a
yield stress. Each, however, gives different values of the fluid parameters depending on
the data range used in the mathematical analysis. The most reliable value of yield stress,
when determined from a mathematical intercept, is found using data taken at low shear
rates. For example, the Casson model has produced results for penicillin broths (Roels,
1974) and for interpreting chocolate flow behavior (Steffe, 1996).
The simplest empiricism for )(
?
?? is the two-parameter power law expression.
This simple relationship states that the plot of the log (shear stress) versus log (shear rate)
is linear. If the slope of this curve, n, is one, the fluid is Newtonian. If n is less than one,
the fluid is pseudoplastic; if greater than one, the fluid is dilatant.
Roels et al. (1974) found that the power law model could not adequately describe
the behavior of a penicillin broth over a large range of shear rates. Another study of
penicillin broth, completed by Bongenaar et al. (1973), indicates the power law model
can be successfully used to describe the rheological behavior over narrow shear rate
ranges.
51
The Herschel-Bulkley model also was used to fit the rheological data. If the
fluid does not have yield stress, the model reduces to the power law model. The three
parameters in this model were highly interdependent with each other (Allen et al., 1990).
Therefore, having wide variability, these parameters did not correlate well with biomass
concentration. Also, as shown by Reuss et al. (1982), there is no significant difference
between the Casson, power law and Herschel-Bulkley models over a limited range of
shear rates.
Measurement Techniques
Rheological measurements of filamentous suspensions using conventional
methods (cone and plate, concentric cylinder, and rotating bob viscometers) can be
difficult due to phase separation and other non-homogeneities (Dronawat et al., 1996).
The impeller method is often employed to measure the rheology of suspensions.
Previous workers assumed that the effective shear rate of such a device is related to the
impeller speed by a fluid-independent constant, but there is evidence that this is not true
for all impellers (Allen et al., 1990; Dronawat et al., 1996). It has been suggested by
Allen that a properly designed helical ribbon impeller might be more appropriate for this
technique.
The complex flow field created by the impeller does not allow the direct
calculation of shear rate (Charles, 1978; Allen et al., 1990). The ?average? shear rate in
the measuring vessel,
avg
?
? , is assumed to be proportional to the impeller speed, N, and
independent of the rheology of the fluid in the vessel.
52
kN
avg
=
?
? (1)
Then it is assumed that the dimensionless power number (
No
p ) is inversely
proportional to the impeller Reynolds number (Re
i
) for Newtonian fluids in laminar flow
regime where the impeller Reynolds number is less than 10:
52
2
Re/
i
iNo
DN
M
cp
?
?
== for Re <10 (12)
?
?
2
Re
i
i
ND
= (13)
where k and c are empirically determined constants. Replacing the viscosity,? in the
impeller Reynolds number with the apparent viscosity of the non-Newtonian fluid,
a
? , at
the average shear rate, solving Equation 12 for the apparent viscosity
3
2
i
a
cND
M?
? = (14)
substituting for
a
? and
avg
?
?
3
2
i
avga
cD
kM?
??? ==
?
(15)
The lack of general applicability of the ?average shear rate? concept and the
inaccuracies arising from its application also can be explained by considering the analogy
between the impeller and the rotating cylinder viscometers. In rotating cylinder
viscometers, the shear rate can be significantly affected by the test fluid?s rheological
properties. For example, for a power law fluid with flow behavior index, n, and with
Couette flow between the cup wall (radius R
0
) and bob (radius R
in
) the ratio of the actual
53
shear rate
?
act
? to the shear rate determined for a Newtonian fluid (n=1),
Newt
?
? , at a given
rotational speed, is
])/(1[
])/(1[
/2
0
2
0
n
in
in
Newt
act
RRn
RR
?
?
=
?
?
?
?
(16)
For an impeller viscometer there also can be a much higher average shear rate in
the vessel and, hence, a higher shear rate than that determined on the basis of using a
shear rate relationship, which is independent of rheological properties.
Ulbrecht and Carreau(1985) determined an analogy between the Coette flow in a
rotating cylinder viscometer and an impeller rotating in the laminar flow regime. They
reported the empirical expression for k:
?
?
?
?
?
? ?
=
?
?
)1/(
/2
223
}
4
]1)/[(
{]/[
nn
n
et
ltt
avg
dDn
HDcD
N ?
?
?
(17)
where D
t
is the tank diameter; H
l
is the height of liquid in the tank; n is the flow behavior
index of a power law fluid, and d
e
is the equivalent diameter of the impeller determined
with the help of the relationship obtained for Newtonian liquids:
]1)/[(/4
Re
2323
?==
ettlt
i
No
dDDHD
p
c ? (18)
The dependence of k on the fluid rheology is evident from comparison of Equations 12
and 17.
Brito-de La Fuento et al. (1992) in their study of a helical ribbon impeller also
suggest the shear rate constant is dependent on the consistency index number, n. These
researchers also employed Equation 6 in their investigation. Comparison of the shear
54
rate constants calculated using the two methods for a consistency index number range of
0.1 to 0.7 produce k that are functions of n.
Impeller Ribbon Viscometer Technique
Newtonian and Non-Newtonian calibration fluids are used to calculate the
constants necessary to relate torque and rotational speed measurements to shear rates and
stresses. The Newtonian fluids are utilized to calculate c, using Equation 6. The
parameter c is then used to transform torque and impeller speed-readings to shear rate and
shear stress data.
Torque and impeller speed measurements are taken for non-Newtonian fluids
calculation of the shear rate constant, k, which allows to determination of the shear stress,
shear rate, and apparent viscosity.
In summary, for the impeller ribbon viscometer technique, the power number of
an impeller is inversely proportional to the impeller Reynolds number (Equation 12).
As the impeller rotational speed increases, the flow gradually changes from laminar to
turbulent, passing through a transition region. Parameter c can be obtained from the
calibration fluids. If the same value for c is assumed to apply to a non-Newtonian fluid,
then this equation can be used to calculate the apparent viscosity of that fluid. The shear
stress can be expressed in Equation 15. Once the parameter c and the shear rate
constant are known, the shear rate and shear stress of the non-Newtonian fluid can be
calculated (Metz et al., 1979). The range of the impeller method is determined by the
minimum and maximum torques that can be measured (Metz et al., 1979).
55
Ruston Impeller Programmed Viscometer
The MCR rheometer has many advantages over the previous techniques. It can
be programmed to operate between controlled shear stress (SS) and controlled shear rate
(SR), an option usually available only in high-end research rheometers. The
device is
based on a rotating or oscillating parallel-plate geometry in combination with a low
friction electronically commutated motor
system. The instrument is also well suited for
investigations into the mixing and stirring behavior of emulsions and dispersions.
Sophisticated RheoPlus software is available for operating the instrument from a
computer. It can be connected either via the RS232 interface or via a LAN?Ethernet
interface directly to the network. Numerous analysis models and automation routines
include a special quality control module.
The rheometers perform a wide range of steady and dynamic tests in both SS
and SR mode. From generating simple flow curves to the dynamic analysis of complex
fluids, melts, and co-polymers, all Physica rheometers offer simple programming and test
setup. Part of the flexibility of the software lies in its ability to mix or chain different
test types together to help simulate process and end-use conditions. SS and SR standard
tests, a combination of rotation and oscillation as well as demanding measurements such
as multiwave tests, time/temperature shifts and the superimposition of oscillation/rotation
can be carried out.
56
III. HIGH SOLIDS ENZYMATIC HYDROLYSIS AND
FERMENTATION OF SOLKA FLOC TO ETHANOL
ABSTRACT
To lower the cost of ethanol distillation of fermentation broths, a high initial
glucose concentration is desired. However, an increase of substrate concentration
typically reduces the ethanol yield due to insufficient mass and heat transfer. In
addition, different operating temperatures are required to optimize enzymatic hydrolysis
(50
o
C) and fermentation (30
o
C). To overcome the incompatible temperatures,
Saccharification Followed by Fermentation (SFF) was employed at relatively high solids
concentrations (10 to 20 per cent) using portion loading method.
Before a full scale system can be designed, fermentation studies are required on a
bench scale. Small scale experiments can be used to predict how a large scale process
will behave. The reactor configuration affects the reaction productivity with data from
bench scale enzymatic hydrolysis and fermentation used to design larger production
reactors.
In this study glucose and ethanol were produced from Solka Floc, first digested by
enzyme at 50
o
C for 48 hours, followed by fermentation. In this process, commercial
enzymes were used in combination with a recombinant strain of Zymomonas mobilis
57
(39679:pZB4L). The effects of the substrate concentration (10 to 20 per cent w/v) and
reactor configuration were investigated. In the first step, the enzyme reaction was
achieved with 30 FPU/g cellulose at 50
o
C for 96 hours. The fermentation was then
performed at 40
o
C for 96 hours. Enzymatic digestibility was 50.7, 38.4, and 29.4
percent at after 96 hours with a baffled Rushton impeller at 10, 15, and 20 per cent initial
solids (w/v), respectively, which was significantly higher than that obtained with a
baffled marine impeller. The highest ethanol yield, 83.6, 73.4, and 21.8 per cent of the
theoretical based on the glucose, was obtained at 10, 15, and 20 per cent substrate
concentration, respectively. This yield corresponds to 80.5 percent of the theoretical
based on the cell biomass and soluble glucose present after 48 hours of SFF. Compared
with traditional SSF process for high solid substrate at 40
o
C, SFF gave a higher ethanol
yield.
58
INTRODUCTION
Demand for petroleum products continue to rise. In 2004, global oil
consumption jumped 3.5 per cent, or 2.8 million barrels per day (USA TODAY, 2005).
The U.S. Energy Information Administration projects demand rising from the current 84
million barrels per day to 103 million barrels by 2015 (BP, 2005). If China and India -
where cars and factories are proliferating - consume oil at just one-half of current U. S.
per-capita levels, global demand would jump 96 per cent, according to Dr. Amos Nur
(Stanford University).
Thus, with the increase of oil consumption, the production of bioethanol is
looking ever more promising. In order to have a significant impact on our current oil
consumption, ethanol must be both inexpensive and plentiful (Zhang et al., 1999).
Lignocellulosic biomass such as agricultural residues, wood, and crops are abundant
renewable materials for the production of sugars as a carbon source for subsequent
fermentation. Of the agricultural residues, corn stover yields have increased
proportionately. About 250 million dry tons of stover is produced each year. For
delivery within a 50 mile radius, $30 to $35/dry ton delivered is a good number
(Hetternhaus et al., 2002). The major cost is baling. A sizable resource for
biochemical production of fuels and chemicals thus remains undeveloped.
The polysaccharide fraction of agricultural residues can be hydrolyzed using acids
or enzymes as catalysts (Zhang et al., 1999; Um, 2002). Cellulases catalyze the
hydrolysis of cellulose, the major structural component of biomass, the most abundant
organic material on earth (Scott, 1994).
Complete hydrolysis of cellulose yields
59
the easily fermentable sugar, glucose, allowing biomass to be a potential renewable
energy source (Fein et al., 1991, Kim et al., 2001). As a result, there is strong interest in
understanding the process of enzymatic cellulose degradation at high initial solid
concentration (Fein et al., 1991).
Typically, as much as 90 per cent or more of the broth is water that must be
removed during SSF. This separation is costly and also produces a large aqueous
stream that must then be disposed of or recycled. A high initial cellulose concentration
combined with a favorable conversion yield of cellulose into soluble sugars reduces the
cost of water removal. When concentrated slurries are processed, the medium mixing/
enzyme homogenization becomes difficult and often results in low bioconversion yields
(L?bbert et al., 2001). This difficulty partly accounts for the lack of literature
concerning fermentation of biomass suspensions at concentrations greater than 10 per
cent (Philippidis et al., 1997). For high-solids saccharification and fermentation, the
reaction rate and bioreactor configuration are of critical importance to the economic
feasibility of a larger scale industrial process, since this unit operation requires the
longest residence time relative to the other major biomass conversion reactions of
enzyme hydrolysis and fermentation. These longer residence times during
saccharification translate into higher operating and capital costs per unit of product output.
However, this approach appears to be the simplest and most economically viable
way to attain suitable ethanol concentrations in the broths for distillation. On the whole,
several process parameters must be optimized: substrate concentration, enzyme-to-
substrate ratio, dosage of the active components (?-glucosidase-to-glucanase ratio) in the
enzymatic mixture, bacteria concentration, and reactor conditions. Lastly, one of the
60
most important factors affecting the overall economics is the compatible temperature
between enzymatic hydrolysis and fermentation process at high slurries during the
saccharification followed by fermentation (SFF) process.
In the present study, enzymatic hydrolysis and fermentation of concentrated Solka
Floc were evaluated at conditions optimal for the highest glucose and ethanol yields
using SFF process in a three-liter bioreactor. The influence of dry matter concentration
and bioreactor configuration on the yield of glucose and ethanol was also investigated.
61
MATERIALS AND METHODS
Raw Material
Solka Floc (Fiber Sale & Development Corporation. Urbana, Ohio), a delignified
spruce pulp, was used as raw material for this research. The composition of this
material, analyzed according to NREL Standard Procedure, is shown in Table III-1.
Table III-1. Initial composition of untreated Solka Floc
Ingredient
Cellulose
(%)
Ash
(%)
Moisture
(%)
The rest
(%)
Solka Floc 88 0.5 5.2 -
Commercial Enzyme
Commercially produced Spezyme CP and Novozyme 188 were used for
enzymatic hydrolysis. The cellulose enzyme Spezyme CP, secreted by Trichoderma
longibrachiatum, formerly Trichoderma reesei, was from Genencor International, Inc.
(Palo Alto, CA). The enzyme had an activity of 82 GCU/g as provided by the
manufacturer and 55 IFPU/mL as determined by NREL standard procedure 006 (Adney
and Baker, 1992). Novozym 188 purchased from Sigma (Cat. No. G-0395) were used
for cellulose hydrolysis with a volume ratio of 4 IFPU Celluclast / CBU Novozyme to
alleviate end-product inhibition by cellobiose.
Microorganism
The organism used for this experimentation was Zymomonas mobilis 39679
(pZB2L4). This organism is a proprietary organism obtained from the National
62
Renewable Energy Laboratories of Golden (CO).
Bench-Scale Bioreactor
The two-liter fermentation tests were conducted in a 3.3-liter bench top, BioFlo
?
3000 bioreactor (New Brunswick Scientific Co. Inc., Edison, New Jersey). BioFlo
?
3000 is a versatile bioreactor that provides a fully equipped fermentation system,
adaptable for cell culture, in one compact package. The fermentor is equipped with four
baffles and two 6-flat-blade Rushton impellers. The stainless steel head plate, bottom
dish, and penetrations are polished to a mirror finish to minimize contamination. The
bioreactor can be employed for batch or continuous culture with built-in controllers for
pH, dissolved oxygen (DO), foam/level, agitation, and temperature. It also includes
pumps for acid, base, antifoam, and nutrient addition.
The pH is measured by a glass electrode (Ingold) and controlled by a Proportional
/Integral/Derivative (PID) controller. The pH controller operates two peristaltic pumps
to maintain the pH value. Sterile air was supplied to the broth through the ring sparger
and was controlled by the needle valve of the flowmeter. A DO electrode (Ingold) was
used to measure the dissolved oxygen concentration. A PID controller controlled the
agitation speed with an optical encoder coupled to the motor shaft. The medium
temperature was measured with a platinum resistance temperature director (RTD) and
controlled by a PID controller. Foam was controlled during the fermentation by the
antifoam probe (conductivity probe), which was located in the headplate and adjustable
in height from the medium surface. The BioFlo
?
3000 unit cannot be sterilized in place,
63
but must be disassembled and sterilized in an autoclave.
Strategy of High Solid Loading on Enzyme Hydrolysis and Fermentation
To maximize the glucose and ethanol concentrations, substrate concentrations
were employed from 10 to 20 per cent on a dry basis, corresponding to cellulose
concentrations of 8 to 17 percent. In several studies for traditional batch enzyme
reaction and fermentation of high substrate concentration (> 10 percent), there is no
visible liquid phase due to complete absorption of liquid by the biomass. In this state no
sugar and ethanol products could be seen for tests between 10 and 20 percent. To
overcome this problem the Solka Floc was added to the reactions in three portions during
both enzyme reaction and fermentation up to the 20 percent final substrate concentration.
The portions were added to the reaction in the initial four hours of the reactions. And
then the inoculum prepared as 10 per cent by volume of the total working volume (two
liters) was transferred into the reactor after enzymatic hydrolysis for 48 hours. The
enzyme loading was 30 FPU per gram of cellulose, supplemented by ?-glucosidase to
prevent product inhibition by cellobiose. The SFF experiments were operated for 96
hours, initially at 50
o
C and finally at 30
o
C. Figure III-1 illustrates the strategy for high
solids loading.
The substrate and nutrient media were autoclaved (120?C for 20 minutes), but the
enzyme solutions were not sterile. The Solka Floc slurry, diluted to different dry
weights of solid material (10, 13, 15, and 20 per cent), was used as substrate.
Enzymatic Hydrolysis
The enzymatic digestibility of Solka Floc was tested in duplicate according to
64
Figure III-1. Strategy of high solid loading on enzyme hydrolysis and fermentation.
III-1a: 5 percent (w/v) suspension (t=0)
III-1b: 5 percent (w/v) suspension (t=0 to 4 hr)
III-1c: Reload 5 percent (w/v) suspension (t=4 hr)
(III-1a)
(III-1b)
(III-1c)
65
the NREL Chemical Analysis and Testing Standard Procedure (CAT) No. 009
(NREL,1996). The substrate concentration ranged from was 100 to 150 grams of
cellulose per liter. Cellulase enzyme (Spezyme CP, Lot 301-05021-011) was kindly
provided by Genencor International.
Excess amounts of ?-glucosidase Novozyme 188, 250 CBU/g of activity were
added to completely convert cellobiose to glucose, i. e., 30 CBU/g cellulose. Cellulase
was added at 15 to 30 FPU/g cellulose. Citrate buffer (0.05 M, pH 4.8, in reaction
mixture) and 8 mL Tetracyline (10mg/mL in 70 percent ethanol) were used to keep
constant pH and prevent microbial contamination, respectively. The total glucose
content after 96 hours of hydrolysis was used to calculate the enzymatic digestibility.
All of the hydrolysis was conducted in 3.3-liter bench top, BioFlo
?
3000 bioreactor.
Using the same method, 250 mL Erlenmeyer flask scale was used as control.
Cell Stock Culture
A recombinant bacterium, Zymomonas mobilis ATCC 39679, carrying the
plasmid pZB4L (designated as Zm 39679:pZB4L), was provided by means of a material
transfer agreement to Dr. Thomas R Hanley by M. Zhang from the National Renewable
Energy Laboratory (NREL, Golden, CO) was used in these studies. Stock culture was
maintained on a Difco Rich-Media (RM) agar medium at 4
o
C and was subcultured every
week to maintain viability. Difco RMGTc medium was used to prepare preculture and
fermentation media. The RMGTc consisted of yeast extract (DIFCO Laboratories Inc,
Detroit, MI) 10 g/L; KH
2
PO
4
, 2 g/L; Tetracycline 20 mg/L; glucose 20 g/L. The stock
solutions of the mineral salts and tetracycline were prepared to 10 times the desired
66
concentration (g/L). Yeast extract was mixed with the mineral salt solutions and
autoclaved separately from the carbohydrate solutions to prevent carmelization of the
media. Tetracycline solutions were sterilized by syringe filtration (Gelman 0.2 ?m pore
size) and any concentrated glucose was added aseptically after autoclaving. Initial pH
was not adjusted, although measurement confirmed that the initial pH was consistently in
the range of 5.0 to 5.8. Cells were stored in stock cultures preserved by mixing 1 mL
culture and 0.5 mL of 60 percent sterile glycerol in a 2 mL cryovial at -70
o
C.
Preliminary Fermentation
Preliminary experiments using recombinant Zymomonas mobilis were conducted
in 500 mL Erlenmeyer flasks to provide the growth pattern of the yeast. The RM broth
solution (200 mL) was sterilized at 121
o
C for 20 minutes in an autoclave (SR-24B,
Consolidated Stills and Sterilizers, Boston, MA). Three loops of the stock culture were
transferred to 200 mL of sterilized RM broth solution and the inoculum was incubated at
30
o
C on a platform shaker (Innova 2000, New Brunswick Scientific Co Inc) agitated at
120 rpm for 24 hours. During the shake flask-flash fermentation, 2 mL-samples were
taken with Eppendorf pipette (series 2000, Brinkmann Instruments) every hour. The
optical density (OD) of the sample was measured at 600 nm with a Spectronic 1001
instrument (Milton Roy, NY). The fermentation was continued until the stationary
phase of the microorganism was reached.
Two-Liter Fermentation
The RM broth was prepared for the two-liter fermentation (21 g of RM broth in
67
1000 mL of deionized water). The pH probe was calibrated before the sterilization.
The pH measuring system was calibrated using two buffer solutions of known pH (4 and
7). The pH measuring switch and the mode switch were set to pH and ZERO,
respectively. The pH probe was immersed into a pH 7 buffer solution and the display
was adjusted to read pH 7 with INC/DEC switch. The complete fermentor assembly
with medium was sterilized at 121
o
C for 20 minutes in an autoclave. After sterilization,
about 200 mL of sterilized water was added to the fermentor through the inoculum port to
make up the volume to 2.0 liters. When the medium cooled down to 30
o
C, the dissolved
oxygen (DO) probe and the selector switch was set to DO and the mode switch was set to
ZERO. The display was adjusted to read zero with INC/DEC switch. The DO probe
cable was connected to the DO probe and the mode switch was set to SPAN. Nitrogen
at 1.0 liter per minute was introduced into the vessel and the agitation speed was set to
500 rpm. When the DO value stabilized after 30 minutes, the DO values were adjusted to
100 percent saturation with the INC/DEC switch.
About 200 mL of starter culture was prepared for inoculation. The cap of the
inoculum port was wiped with ethanol-soaked paper towel and screwed back in place.
The fermentation was conducted at 30
o
C at pH 5. The temperature of the fermentor was
controlled by cooling /hot water in the jacket of the fermentor. The pH of the broth was
adjusted with automatic periodic addition of 1.0 M NaOH. Nitrogen gas was supplied
to the fermentation medium through the sparger ring under the six-blade impeller. The
gas rate was set at 100 milliliters per minute using a rotameter. This flow rate was
equivalent to 0.1 volume of air per volume of medium per minute (0.1 VVM).
68
ANALYSIS AND ASSAY
Carbohydrates
NREL standard procedure No. 002 was used to determine the quantity of
cellulose in the solid Solka Floc. A 0.3 g sample of the biomass was treated at 30
o
C
with 72 per cent sulfuric acid for two hours and then autoclaved at 121
o
C after diluting
the acid to four per cent with deionized water. Both reagent grade glucose and xylose
were autoclaved together in order to calibrate the amount of sugar decomposed during the
reaction. The hydrolyzate was centrifuged at 15,000 rpm. After centrifuging, the
hydrolyzate was tested on YSI for glucose. The YSI model 2700 glucose analyzer (YSI,
Yellow Spring, Ohio) was used as the standard laboratory analyzer that employs the
glucose oxidase method to quantify glucose concentration.
Moisture and Ash
National Renewable Energy Laboratory (NREL) Standard Procedure No. 001
and 006 were followed. A 1.5 g sample of the biomass was weighed in an aluminum
pan and dried in a convection oven at 105 ? 3
o
C for over four hours. The oven-dried
sample was cooled in a desiccator and weighed to obtain the weight difference caused by
moisture. The initial materials used in every experiment contained 5.2 percent moisture.
After determination of total solids and moisture in biomass, a 1.0 g sample of the
biomass was weighed in an ignitable ceramic crucible and brought to constant weight by
igniting at 575 ? 25
o
C. The crucible was removed from the furnace, cooled to room
temperature in a desiccator, and weighed to the nearest 0.1 mg. The initial materials
used in every experiment contained 0.5 percent ash.
69
Dry Cell Weight vs. Optical Density
Dry cell weight (DCW) is necessary for the determination of several key
parameters in these fermentation studies. These values were found by determining a
linear correlation between the total cell mass and the absorbance of visible light at 600
nm (OD
600
) across a 1 cm light path. Yellow colored growth media has minimal absorption at 600 nm,
so using that wavelength gives you a minimal contribution from the media blank.
Samples of Zymomonas 39679 pZB4L were collected in the late exponential
phase. A potion of these samples was saved for OD analysis. The remainder was
centrifuged (Sorvall RC-5B, Du Pont instruments) for 10 minutes at 15,000 rpm. The
resulting supernatant was removed, and the biomass pellet was resuspened in DI water
and centrifuged again. This washing procedure ensured the removal of all noncellular
material that could be potentially reactive with the biomass during drying. The washing
procedure was repeated twice, and the biomass pellet and deionized water was added to
previously dried and tared aluminum dishes. These samples were dried in a desiccator
at 40
o
C for 12 hours and allowed to equilibrate to room temperature in a dry box for 3
hours before the mass was measured. The total cell mass was then determined and
divided by the original sample volume to give the cell concentration.
The samples that were set aside for DCW measurement were diluted to various
concentrations and the DO
600
was taken with the spectrophotometer (Spectronic 20
GENESYS Spectrophotometer) zeroed on curvette blank containing deionized water as
reference. Any OD
600
measurement larger than 0.6 was considered out of the linear
range of the calibration with more dilution required.
70
Glucose and Ethanol
Aliquots were taken from the sample port every hour for 24 hours. A 10 mL
plastic syringe attached to the sampling port facilitated the sampling procedure. A four-
drum (15 mL) vial was tightened to the sampling port, the sample valve was opened and
the syringe slowly released. When the desired volume of sample was obtained (5 mL),
the sample valve was closed. The cell mass concentration for 2 mL samples was
measured in the Spectronic 20 GENESYS Spectrophotometer using the calibration curve
previously determined. About 1.5 mL of the sample was centrifuged (15000 rpm for 10
minutes) and the supernatant was used for glucose and ethanol analysis. The dissolved
oxygen saturation values (DO) were recorded immediately after taking the sample. The
ethanol yield and digestibility was calculated for the glucose and ethanol following the
procedure of NREL Chemical Analysis and Testing Standard Procedures No. 008 and
009.
71
RESULTS AND DISCUSSION
Concentrated solid saccharification following fermentation can be roughly
defined as beginning at the insoluble solids level where free liquid is no longer present in
the slurry such that the separation of a liquid and solid phase from the suspension is not
spontaneous. The presence of free water in high-solids slurries depends strongly on
both the insoluble solids level and the cellulose content of the solids, which influences
lignocellulosic-water interactions and cellulose swelling. Alternatively, this
concentrated suspension definition can be regarded as the solids region where the slurry
viscosity is highly non-Newtonian (Pimenova et al., 2003). For Solka Floc substrates,
this region begins at approximately 10 per cent to 20 per cent (w/v) insoluble solids.
Performing the saccharification following fermentation at high insoluble solids
introduces a new set of process-related problems associated with slurry mixing, method
of substrate loading, and effectiveness of tank configuration.
Enzymatic Hydrolysis Below 10 Percent (w/v)
Figure III-2 shows cellulose conversion to ethanol for the two-liter batch
hydrolysis in the initial six-hour enzyme reaction with other enzyme parameters (T and
enzyme loading) held constant. The experiments at five per cent initial insoluble solids
by weight and 30 FPU/g cellulose with 30 CBU/g cellulose addition of Novozym to
reduce cellobiose inhibition. This plot shows that the conversion rates exhibit linear
behavior at the beginning of the reaction, thus cellulosic material was sufficiently
liquidized in four hours.
Many researchers have shown that the initial rate of hydrolysis is much higher
72
y = 2.0455x + 15.75
R
2
= 1
y = 1.8389x + 12.113
R
2
= 0.9816
0
5
10
15
20
25
30
35
02468
Time (hour)
G
l
uc
o
s
e
C
o
nv
e
r
s
i
o
n
[
%
]
Rushton Impeller
Marine Impeller
Figure III-2. The glucose conversion rate during the initial 6-hour enzymatic hydrolysis
of 5 per cent (w/v) Solka Floc
Note:
1. Enzymatic hydrolysis condition: six hours, 30 FPU/g cellulose, pH 4.8
to 5.0, 50
o
C, 120 rpm.
2. All data points given are the average yields for the duplicate
determinations.
73
than the subsequent rate (Zhang and Lynd, 2004), while proposed reasons include
selective initial hydrolysis of amorphous cellulose (Davis et al., 2002; Mansfield et al.,
1999), decreases in specific enzyme adsorption or subsequent inability of bound
cellulases to reach new catalytic sites (Lynd et al., 2002; Eriksson et al., 2002), and steric
preferences (V?ljam?e et al., 1998; Ooshima et al., 1990; Converse et al., 1990). The
specific rate at which the majority of the cellulose is utilized remains approximately
constant until a critical value is reached at approximately 80 per cent cellulose conversion.
However, in order to overcome of poor mass and heat transfer the Solka Floc was
added to the reaction in three portions in four hours during enzymatic hydrolysis up to 20
per cent final substrate concentration.
The enzymatic digestibilities as function of time for each lower solid
concentration are shown in Figure III-3a. The digestibility was 79, 68, and 63 per cent
at 1, 3, 5 per cent solids concentration, respectively. The conversion yields were, in
general, higher at the lower substrate concentration (~ 5 percent, w/v) because of lower
mass transfer limitations within the reaction medium. Figure III-3b shows percent of
glucose conversion during enzymatic hydrolysis as a function of impeller type. On
average, the baffled Rushton bioreactor gave 63 per cent conversion of cellulose at the 5
per cent solid concentration, which generated 5 per cent more glucose than baffled
marine bioreactor.
74
0
10
20
30
40
50
60
70
80
90
0 1224364860728496
Time (hour)
G
l
uc
os
e
C
o
nve
r
s
i
on
[
%
]
1 % Solka Floc
3 % Solka Floc
5 % Solka Floc
0
10
20
30
40
50
60
70
0 1224364860728496
Time (hour)
G
l
u
co
s
e C
o
n
v
ers
i
o
n
[
%
]
Rushton with baffles
Marine with baffles
Figure III-3. Enzymatic hydrolysis of Solka Floc at lower percent solid concentration (III-3a) and Solka Floc at 5 per cent(w/v)
solid for different impeller type (III-3b) as a function of time at constant cellulase activity.
Note:
1. Enzymatic hydrolysis condition: 96 hours, 30 FPU/g cellulose, pH 4.8~5.0, 50
o
C, 120 rpm.
2. All data points given are the average yields for the duplicate determinations.
(III-3a) (III-3b)
75
Stirring Power in Three-Liter Fully-Baffled Tanks
The generally accepted measurements of stirring power in fully-baffled tanks
containing non-Newtonian fluids with various impellers were made by Rushton, Costich,
and Everett (1950). Pitched-blade turbine stirring power measurements in Newtonian
fluids were made by Bates, Fondy and Corpstein (1963). Metzner and Otto (1957)
measured power consumption for shear-thinning, non-Newtonian fluids with little
viscoelastic response (mainly carboxymethyl cellulose (CMC) and Carbopol in vessels
stirred by disk turbines. They correlated their data using a Reynolds number corrected
for the shear thinning viscosity computed at a shear rate based on a constant KMO (=13)
times rotation rate N. Their data showed a mild power reduction from the Newtonian
data in the transition regime from laminar to turbulent agitation.
Figure III-4 show the power consumption for the baffled three-liter bioreactor at 5
per cent Solka Floc suspension with a working volume of two liters. Figure III-4a
shows that the measurements were made with a 0.046 m diameter Rushton impeller, with
and without wall baffles. The power consumption increases with increasing rotational
speed, and power consumption was same up to 400 rpm with and without wall baffles.
A significant difference of power consumption was seen beginning at 600 rpm. Figure
III-4b shows results obtained with Rushton and marine impellers, respectively. At the 5
per cent solid suspension, power consumption is significantly lower for marine impeller
than for Rushton impeller. Figure III-5 is analogous to Figure III-4 and shows the
power consumption at a relatively high solid concentration (15 percent, w/v) and
otherwise unchanged operating conditions. This diagram reflects difference in power
consumption between impellers in this type of baffled mixing tank. Specifically, power
76
y = 9E-08x
2.8245
R
2
= 0.9946
y = 2E-07x
2.6544
R
2
= 0.9807
0
5
10
15
20
25
30
0 200 400 600 800 1000 1200
RPM
Po
w
e
r
(
W
)
With 4 set baffles
Without baffles
y = 9E-08x
2.8245
R
2
= 0.9946
y = 1E-05x
1.8503
R
2
= 0.9952
0
5
10
15
20
25
30
0 200 400 600 800 1000 1200
RPM
P
o
w
er (
W
)
Rushton with baffles
Marine with baffles
(III-4a)
Figure III-4. Power consumption in the two-liter bioreactor with 5 per cent (w/v) Solka Floc Suspension with various
bioreactors configurations as function of RPM.
Note:
1. Enzymatic hydrolysis condition: 30 FPU/g cellulose, pH 4.8~5.0, 50
o
C.
2. Figure III-4a: Rushton impeller with or without baffles, Figure III-4b: baffled with Rushton or Marine
impeller.
3. All data points given are the average yields for the duplicate determinations.
(III-4b)
77
y = 2E-07x
2.75
R
2
= 0.994
y = 5E-07x
2.5484
R
2
= 0.998
0
5
10
15
20
25
30
35
0 200 400 600 800 1000 1200
RPM
Po
w
e
r
(
W
)
With 4 set baffles
Without baffles
(III-5a)
y = 2E-07x
2.75
R
2
= 0.994
y = 2E-05x
1.7757
R
2
= 0.9984
0
5
10
15
20
25
30
35
0 200 400 600 800 1000 1200
RPM
P
o
w
er (
W
)
Rushton wth baffles
Marine with baffles
(III-5b)
Figure III-5. Power consumption in two-liter bioreactor with 15 per cent (w/v) Solka Floc Suspension with various bioreactors
configurations as function of RPM.
Note:
1. Enzymatic hydrolysis condition: 30 FPU/g cellulose, pH 4.8~5.0, 50
o
C.
2. Figure III-5a: Rushton impeller with or without baffles, Figure III-5b: baffled with Rushton or Marine
impeller.
3. All data points given are the average yields for the duplicate determinations.
78
consumption result in the same trend as seen with Figure III-3. At an agitation speed of
900 rpm for Rushton bioreactor, power consumption is much higher than that of 5 per
cent solid concentration. This result implies that fluid flow and power consumption
increases with increasing viscosity. The 1000 rpm agitation speed required power
inputs of 30 and 3.6 W for the baffled Rushton turbine and the baffled marine
configuration, respectively. Figure III-5a shows the power consumption for the Rushton
impeller with and without baffles, with the power required for the baffled configuration
larger by 10 W at 15 per cent solids concentration.
These findings demonstrate the similarity of hydrodynamic effects caused by
viscosity and baffles; both adding a resistance to flow. The effect is the same - more
mechanical power must be introduced to the system to maintain fluid motion.
The Effect of Bioreactor Configuration on Bioconversion
The bioreactor was tested with a 4.6 cm diameter Rushton and 1.0 cm diameter
marine impeller for glucose conversion, with or without wall baffles to give four
bioreactor configurations: Rushton, baffled Rushton, marine, and baffled marine. The
Rushton impeller had six blades; each blade was 1.0 cm (height) by 1.5 cm (width) by
0.05 cm (thickness). The marine impeller had three inclined curved blades of standard
configurations. Each of four wall baffles was 17. 5 cm (height) by 1.5 cm (width) by
0.05 (thickness). The substrate used for the hydrolysis studies was shown to be
primarily composed of cellulose, since glucose constituted the majority of the sugars in
the substrates (88 percent). Figure III-6 illustrates the effect of baffles on glucose
conversion for a Rushton turbine and 5 and 10 per cent solids. Baffles become more
79
important for glucose conversion as the solids concentration increases. Figure III-7
shows the enzymatic digestibility (g released glucose/g initial cellulose) studies suggested
by the NREL standard procedure for various reactor configurations at 5 per cent solids
concentration. All of the portion methods are performed starting with 5 per cent solids
concentration. The baffled Rushton bioreactor produced 10 per cent more glucose than
the baffled Marine bioreactor (Figure III-3).
Mixing efficiency in a bioreactor for glucose and ethanol production is affected
by various numbers of parameters such as baffles, impeller speed, impeller type,
clearance, tank geometry, solubility of substance, and eccentricity of the impeller
(Oldshue, 1983). A vortex is generated owing to centrifugal force acting on the rotating
suspension. If the vortex reaches the impeller severe air entrainment occurs. The
depth and the shape of the vortex depend on impeller and vessel dimensions, the
rotational speed and the presence of baffles.
Figure III-8 show results of cellulose digestibility with and without baffles at
relatively high solids concentration (13 and 15 per cent, w/v). As expected, baffled
configurations gave higher glucose conversion than unbaffled configuration at 13 and 15
per cent solids concentration. In baffled tanks, a better concentration distribution
throughout the tank and therefore improvement in the mixing efficiency is achieved to
yield high glucose conversion at high solid substrates. In the unbaffled vessel with the
impeller rotating in the center, centrifugal force acting on the fluid raises the fluid level at
the wall and lowers the level at the shaft.
80
0
10
20
30
40
50
60
70
0 1224364860728496
Time (hour)
G
l
u
c
os
e
C
o
nve
r
s
i
o
n [
%
]
With baffles
No baffles
(III-6a)
0
10
20
30
40
50
60
0 1224364860728496
Time (hour)
G
l
uc
os
e
C
o
nve
r
s
i
on [
%
]
With baffles
No baffles
(III-6b)
Figure III-6. The effect of baffles on glucose conversion of Solka Floc at 5 (III-6a) and 10 per cent (III-6b) solids concentration
with a Rushton impeller as a function of time at constant cellulase activity.
Note:
1. Enzymatic hydrolysis condition: 96 hours, 30 FPU/g cellulose, pH 4.5 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in one portion during fermentation up to 10 % final substrate concentration
(III-6b)
3. All data points given are the average yields for the duplicate determinations.
81
0
10
20
30
40
50
60
70
0 1224364860728496
Time (hour)
G
l
u
c
o
s
e C
o
n
v
ers
i
o
n
[
%
]
Rushton with baffles
Marine with baffles
Figure III-7. The glucose conversion as a function of time for the two bioreactors:
baffled Rushton and baffled Marine configuration at 5 per cent (w/v) Solka Floc.
Note:
1. Enzymatic hydrolysis condition: 96 hours, 30 FPU/g cellulose, pH 4.8
to 5.0, 50
o
C, 120 rpm.
2. All data points given are the average yields for the duplicate
determinations.
82
0
10
20
30
40
50
0 1224364860728496
Time (hour)
G
l
uc
o
s
e
C
o
nve
r
s
i
o
n [
%]
With baffles
No baffles
(III-8a)
0
10
20
30
40
50
0 1224364860728496
Time (hour)
G
l
uc
o
s
e
C
o
nv
e
r
s
i
o
n
[
%
]
With baffles
No baffles
(III-8b)
Figure III-8. The effect of baffles on glucose conversion of Solka Floc at 13 (III-8a) and 15 per cent (III-8b) solids
concentration with baffled Rushton bioreactor as a function of time at constant cellulase activity.
Note:
1. Enzymatic hydrolysis condition: 96 hours, 30 FPU/g cellulose, pH 4.8 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in two portions during enzymatic hydrolysis up to 13 % and 15 % final
substrate concentration.
3. All data points given are the average yields for the duplicate determinations.
83
0
10
20
30
40
50
60
0 1224364860728496
Time (hour)
G
l
uc
o
s
e
C
o
nve
r
s
i
o
n [
%
]
180 RPM
120 RPM
60 RPM
(III-9a)
0
10
20
30
40
50
60
70
0 1224364860728496
Time (hour)
G
l
uc
o
s
e
C
o
nv
e
r
s
i
o
n [
%
]
180 RPM
120 RPM
60 RPM
(III-9b)
84
0
10
20
30
40
50
0 1224364860728496
Time (hour)
G
l
u
c
o
s
e C
o
n
v
er
si
o
n
[
%
]
180 RPM
120 RPM
60 RPM
(III-9c)
Figure III-9. The effect of RPM on enzymatic hydrolysis of Solka Floc at 10 (III-
9a), 13 (III-9b) and 15 per cent (III-9c) solid concentration with baffled Rushton
bioreactor as a function of time at constant cellulase activity.
Note:
1. Enzymatic hydrolysis condition: 96 hours, 30 FPU/g cellulose, pH 4.8 to
5.0, 50
o
C.
2. Substrates were added to the reactions in portions during enzymatic
hydrolysis up to 10, 13, and 15 percent final substrate concentration.
3. All data points given are the average yields for the duplicate
determinations.
85
The Effect of Rotational Speed on Glucose Yield
One important parameter in the current bioreactor design is the rotational speed of
the impeller. Filamentous fungal fermentations in a stirred tank bioreactor usually
experience the high apparent viscosity and the non-Newtonian broth behavior that can
lead to the use of high agitation speed to provide adequate mixing and oxygen transfer in
order to improve the cell and ethanol production rate. However, mycelial damage at
high power input and rigorous agitation can limit the acceptable range of agitation speed.
This damage is probably results from the higher shear rates present at the impeller tip.
Therefore, the high rate of cell damage could lower growth and product formation
(Amanullah et al., 2002; Li et al., 2000; Tamerler and Keshavarz, 1999).
Similarly, for enzyme suspensions in the stirred tank bioreactor, the rotational
speed of the fibrous matrix influenced glucose conversion by cellulase. Due to the high
shear rate around impeller, the higher rotational speed than 200 rpm could damage the
enzyme and/or the Zymomonas mobilis. Therefore, the effect of rotational speed was
determined at the levels of 60, 120, and 180 rpm at solid concentrations of 10, 13, and 15
per cent respectively. Figure III-9 shows the glucose conversion of the enzymatic
hydrolysis at different rotational speeds at various solid concentrations. It was found
that the rotational speed tested in this study did not greatly affect glucose yield between
120 rpm and 180 rpm; however, the increase in glucose productivity was observed at
high rotational speeds (Figure III-9a, b, and c). It was also found that low rotational
speeds (60 rpm) tested in this study did not appear to be sufficient to produce contact
between the substrate and the enzyme. Consequently, conversion yields were 5 to 10
percent lower than those obtained under 120 and 180 rpm. There was no significant
86
difference in conversion for various rotational speeds at relative low solid concentration.
This result was probably due to the substantial decrease in the viscosity of the reaction
mixture and better interaction between the enzymes and the remaining substrates. In
addition, Figure III-9 shows profiles for rotational speeds. Tests revealed no significant
effect of mixing speed in the range 120 to 180 rpm on the glucose conversion after 96
hours. Low power inputs for mixing are therefore possible. Thus, in this project, a
rotational speed setting of 120 rpm was selected for ethanol fermentation and CFD
simulation. This result suggests that a threshold value of the rotational speed (mass
transfer related) has to be achieved for efficient glucose conversion.
87
0
10
20
30
40
50
60
0 1224364860728496
Time (hour)
G
l
u
co
s
e C
o
n
v
ers
i
o
n
[
%
]
10 % Solka Floc
13 % Solka Floc
15 % Solka Floc
20 % Solka Floc
(III-10a)
0
10
20
30
40
0 1224364860728496
Time (hour)
G
l
u
co
s
e C
o
n
v
ers
i
o
n
[
%
]
10 % Solka Floc
13 % Solka Floc
15 % Solka Floc
20 % Solka Floc
(III-10b)
Figure III-10. The enzymatic hydrolysis for the baffled Rushton turbine (III-10a) and the marine propeller (III-10b) as a
function of time at constant cellulase activity.
Note:
1. Enzymatic hydrolysis condition: 96 hours, 30 FPU/g cellulose, pH 4.8 to 5.0, 120 rpm.
2. Substrates were added to the reactions in portions during enzymatic hydrolysis up to 20 percent final substrate
concentration.
3. All data points given are the average yields for the duplicate determinations.
88
High-Solids Saccharification by Portion Loading
To improve process economics of the lignocellulosic biomass to ethanol process,
a bioreactor system for enzymatic saccharification at high solids concentrations was
developed. The saccharification was performed in a three-liter bioreactor (New
Brunswick Scientific Co. Inc., Edison, New Jersey) with a baffled Rushton turbine and a
baffled marine propeller. As discussed in the material and method section, substrates
were added to the reactions in portions during enzymatic hydrolysis up to 20 percent
(w/v) final substrate concentration.
During batch system saccharification in bench scale fermentor, the solids
concentration is one of the most important variables affecting the rate and extent of
conversion. Figure III-10 shows the effects of Solka Floc solids loading on rates and
extent of glucose conversion for increasing solids levels with two different reactor
configurations at the constant enzyme loading. From the data shown in Figure III-10a
for the 30 FPU/g cellulose loading, it is apparent that increasing the solids loading up to
20 percent (w/v) significantly decreases the rate of hydrolysis and the conversion of
glucose. The most likely reasons for this decrease in rate and conversion are a
combination of cellobiose and glucose inhibition of the enzyme system from the
correspondingly higher sugar levels reached using higher solids and mass transfer
limitations. Specifically, in case of mass transfer, a relatively weak axial flow was
found near the center bottom of the tank and below the baffle from CFD simulation.
Figure III-10a shows that for all cases presented, glucose concentrations in the
liquid phase greater than 41 g/L are achievable, and, for the case of 20 per cent solids,
over 50 g/L of glucose is attained after 48 hours. However, higher solids loadings
89
require significantly longer residence times to achieve these high liquid phase sugar
levels. As in previous work (Um, 2002), the effect of glucose on ?-glucosidase activity
is the most important inhibition concern, and this can become the ultimate rate limiting
step when glucose accumulates to very high levels. At 20 per cent solids concentration,
the glucose digestibility is lower than at 10 per cent solids concentration, indicating that
mixing limitations for this level of solids has become a significant factor in addition to
glucose inhibition. On average, the baffled Rushton bioreactor had higher glucose
conversion than baffled marine configuration by as much as 15 per cent (Figure III-10b).
Most importantly, this work also demonstrates that cellulose conversions greater
than 20 percent can be achieved at initial insoluble solids levels as high as 20 percent by
portion loading method. No continuous liquid phase exists at a concentration of 20 per
cent in the bioreactor without portion method. Shear stress is 10 times higher than that
of 20 per cent loaded by portion method (Appendix Table B-7). This result indicates
that flowability depends on the presence of a continuous liquid phase.
90
0
10
20
30
40
50
60
0 1224364860728496
Time (hour)
G
l
u
co
s
e C
o
n
v
ers
i
o
n
[
%
]
50 ?C
40 ?C
30 ?C
Figure III-11. The effects of temperature on high solid enzyme hydrolysis.
Note:
1. Enzymatic hydrolysis condition: 96 hours, 30 FPU/g cellulose, pH 4.8 to
5.0, 120 rpm.
2. Substrates were added to the reactions in portions during enzymatic
hydrolysis up to 10 per cent final substrate concentration.
91
0
2
4
6
8
10
0 6 12 18 24 30 36 42 48
Time (hour)
E
t
ha
no
l
(
g
/
L
)
Ethanol Concentration @ 30 ? C
Ethanol Concentration @ 40 ? C
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
0 6 12 18 24 30 36 42 48
Time (hour)
O
D
@
600
n
m
0
5
10
15
pH
Cell Growth at 30 ? C
Cell Growth at 40 ? C
pH at 30 ? C
pH at 40 ? C
(III-12a)
(III-12b)
Figure III-12. The effects of temperature on cell growth curve and ethanol production.
Note:
1. Yeast extract =0.1 g/L, KH
2
PO
4
= 0.02 g/L, DI water = 9 mL, 20 per cent glucose stock solution = 1 mL, and Zm.
mobilis = 5~10 colonies: RM liquid for growing bacterial in 10 mL solution.
2. All data points given are the average yields for the duplicate determinations.
92
The Effects of Temperature on High-Solid Bioconversion
Operating temperature is another significant parameter that strongly affects
enzyme stability as well as bacterial activation on enzymatic hydrolysis and fermentation.
For SFF process, it is necessary to compromise with a temperature that is tolerable for
microbial fermentation but still provides a reasonable hydrolysis rate. Figure III-11
shows the effect of temperature on enzyme reaction with a baffled Rushton turbine for 10
per cent (w/v) initial insoluble solids. By comparing the conversion profiles for the
50
o
C, 40?C and 30?C experiments, it is clear that long-term exposure to a temperature of
30?C and 40
o
C does not result in activation of enzymes as has been previously suspected.
For a baffled Rushton bioreactor, operation at 30?C (the temperature that Zm. mobilis SSF
is typically performed) decreases the conversion of glucose by nearly 20 per cent relative
to conversion at 50?C.
To investigate the effect of temperature on cell growth mode, cell density and
ethanol concentration were examined
in stationary cultures for 48 hours at 30?C and 40
o
C.
Figure III-12 shows the optical density of the cells, the pH curve, and ethanol
concentration. From the figures, it is clear that the temperature for bacterial
fermentation is a major factor affecting the rate of cell growth as might be supposed.
The maximum ethanol concentration for the specific cell growth rates is comparable to
those from Figure III-12 - approximately 8 g/L and 4 g/L for the temperature of 30
o
C and
40
o
C respectively. Figure III-12a demonstrates that the value of pH decreased with
increasing the rate of cell growth curve.
An important consideration for the cell growth results is that at 40?C, the
microorganism failed to ferment glucose to ethanol. The ethanol concentration was
93
0
10
20
30
40
50
0 1224364860728496
Time (hour)
C
o
n
cen
t
r
a
t
i
o
n
[
g
/
L
]
Glucose
Ethanol
0
10
20
30
40
50
60
0 1224364860728496
Time (hour)
C
o
n
c
e
n
tr
a
ti
o
n
[g
/
L
]
Glucose
Ethanol
(III-13a)
(III-13b)
94
0
10
20
30
40
50
60
0 1224364860728496
Time (hour)
C
o
n
c
e
n
t
r
a
t
io
n
[
g
/L
]
Glucose
Ethanol
(III-13c)
Figure III-13. The time course of substrate utilization and ethanol production by
Zymomonas mobilis at 10 % (III-13a), 15 % (III-13b), and 20 % (III-13c) solid
concentration with a Rushton impeller as a function of time at constant cellulase
activity.
Note:
1. Enzymatic hydrolysis condition: 48 hours, 30 FPU/g cellulose, pH 4. to 5.0,
50
o
C.
2. Cell transfer after 48 hours, Initial OD
600
0.52.
3. Fermentation condition: 96 hours, Zymomonas mobilis (39679:pZB4L), pH
5.0, 30
o
C.
4. Substrates were added to the reactions in portions during enzymatic
hydrolysis up to 20 % final substrate concentration.
5. All data points given are the average yields for the duplicate determinations.
95
decreased by nearly 50 per cent relative to 30
o
C after 2 days.
The Effect of Substrate Concentration on Ethanol Yield
In several studies it was found that for conventional fermentation of
lignocellulosic biomass, the content of solids is initiated to about 10 per cent, resulting in
a maximum ethanol concentration of 4 per cent (v/v). However, if higher solids levels
could be fermented, it might be possible to achieve higher ethanol concentration reducing
downstream cost (Varga, et al., 2004; Mohagheghi et al., 1992; Spindler et al., 1988).
SFF baffled Rushton bioreactors were operated in the three-liter fermentor after
enzymatic hydrolysis (after 48 hours) using a maximum 20 per cent DM, corresponding
to cellulose concentration 17 percent. As previous mentioned, to avoid poor mass and
heat transfer, the substrate was added to the reaction in three portions during enzymatic
prehydrolysis up to 20 per cent final substrate concentration. The portion was added to
the hydrolysis in four-hour intervals (Figure III-2). The rate of enzymatic hydrolysis
after four hours was dramatically high, thus the initial five percent substrate was
sufficiently liquidized to allow the loading of another 5 per cent substrate (Figure III-1).
The effect of substrate concentration on the ethanol yields at 96 hours SFF using
Solka Floc is shown in Table III-2 and Figure III-14. Figure III-13 shows that the time
course of substrate utilization and ethanol production by Zymomonas mobilis at 10 (III-
13a), 15 (III-13b), and 20 percent (III-13c) solid concentration with Rushton impeller as a
function of time at constant cellulase activity. Increasing the DM content to 20 percent,
the ethanol yield was dramatically reduced at a substrate concentration of 20 per cent DM
compared to 10 and 15 per cent solid concentration, which is probably due to insufficient
96
Table III-2. Ethanol yield and conversion (%) for Zm. mobilis after 48hours
10 % 15 % 20 %
Initial glucose after enzyme
reaction (g/L)
42.6 55.5 58.4
Final ethanol concentration
at 48 hours (g/L)
18.2 19.7 6.3
Conversion of the consumed
glucose to ethanol (%)
83.6 73.4 21.8
Theoretical ethanol yield (%)
80.5 68.6 19.1
Total Fermentation time with
portion method (hours)
106 110 114
mass transfer caused by different viscosity and flow patterns for those concentrations.
At relatively high substrate concentrations the enzymes could not liquefy the
cellulose fibrous material, and a low enzyme reaction rate resulted in low ethanol yields
of nearly 21 percent. The maximum ethanol yield of 83 percent was achieved at 10 per
cent solid concentration after 48 hours. However, the ethanol yields were also favorable
at 73 percent, using 15 percent solid concentration.
Figure III-14 further shows that at a 30 FPU/g cellulose enzyme loading at 40
o
C
which was chosen to overcome incompatible temperature during SSF, the process
provides no significant improvement in either rate or extent of conversion over high
solids Solka Floc. An important consideration for the SSF results is that at 40?C the
microorganism failed to ferment glucose to ethanol at reasonable pH (~5.0) after 4 days.
The ethanol yields were below 10 per cent relative to all of substrate concentration.
97
0
20
40
60
80
100
0 1224364860728496
Time (hour)
C
Et
O
H
[%]
10 % Substrates
15 % Substrates
20 % Substrates
(III-14a)
0
2
4
6
8
10
0 1224364860728496
Time (hour)
C
Et
O
H
[%
]
10 % Substrates
15 % Substrates
20 % Substrates
(III-14b)
Figure III-14. The ethanol conversion yield (C
EtOH
) with Rushton impeller as a function of retention time during SFF at 30
o
C
(III-14a) and 40
o
C (III-14b) with 10, 15, and 20 percent substrate concentration and constant cellulase activity. Note: same as
condition of Figure III-13.
98
CONCLUSIONS
This project demonstrated a number of important novel conclusions related to
high solids saccharification following fermentation systems. First, critical to the design
and optimization of agitated bioreactor processes is understanding and assessing the
effect of reactor configuration on glucose and ethanol production. In this investigation,
high-solids enzymatic saccharification was performed under a two liter working volume
in various bioreactor configurations. At 120 rpm, reactors with Rushton impellers
achieved much higher concentrations of glucose when compared to marine impellers. The
result of enzymatic hydrolysis indicated that wall baffles significantly increased
digestibility in the Rushton bioreactor but not in the marine bioreactor. Therefore, the
baffled Rushton bioreactor should be used for high-solid bioconversion process.
Second, it was demonstrated that sugar inhibition of enzymatic saccharification
rates is not as compelling a concern as had previously been suspected, and that
remarkably high concentrations of glucose are achievable in high solids enzymatic
reactions. Besides sugar inhibition, other parameters including bioreactor configuration
and temperature were identified as important for high-solids enzymatic saccharification.
This remarkable improvement in rate, in addition to the high product concentrations, has
the potential to greatly improve the economics of enzymatic saccharification.
Most importantly, this project also demonstrated that glucose concentration and
ethanol conversion was greater than 50 g/L and 20 per cent at 20 per cent solid
concentration respectively, which is likely due to adopting an optimal substrate loading
strategy.
99
The portion loading method provides a feasible method for fermenting cellulosic
material while avoiding mass transfer limitation at higher solids loading. In traditional
batch fermentation, there is visually no continuous liquid phase a concentrations of 20 per
cent in the bioreactor, which is likely due to complete absorption of liquid by the biomass
before reacting between microorganism and substrates.
An additional observation from this work is that it is important to keep this
fermentation anaerobic, even though increased cellulosic biomass concentration is
desirable in SFF process. Small amounts of acetate and glycerine are produced. If
oxygen is introduced to the reaction, the production of acetate and glycerine increases,
thus decreasing the purity of the desired ethanol product.
100
IV. RHEOLOGICAL PARAMETER DETERMINATION FOR
ENZYMATIC SUSPENSIONS AND FEMENTATION BROTHS
WITH HIGH SUBSTRATE LOADING
ABSTRACT
Traditionally, as much as 80 percent or more of an ethanol fermentation broth is
water that must be removed. This mixture is not only costly to separate, but also
produces a large aqueous stream that must then be disposed of or recycled. Integrative
approaches to water reduction include increasing the biomass concentration during
fermentation.
In this paper experimental results are presented for the rheological behavior of
high-solids enzymatic cellulose hydrolysis and ethanol fermentation for biomass
conversion using Solka Floc as the model feedstock. The experimental determination of
the viscosity, shear stress, and shear rate relationships of the 10 to 20 per cent slurry
concentrations with constant enzyme concentrations are performed with a variable speed
rotational viscometer (2.0 to 200 RPM) at 40
o
C and combined temperature (50
o
C, 30
o
C)
for the initial four hours. The viscosities of enzymatic suspension observed were in
range of 0.0418 to 0.0144, 0.233 to 0.0348 and 0.292 to 0.0447 Pascal-seconds for shear
rates up to 100 reciprocal seconds at 10, 15, and 20 per cent initial solids (w/v),
respectively.
101
The average particle size during the enzymatic treatment and fermentation process of
Solka Floc at 40 ?C and combined temperature (50 to 30?C) was approximately 57.8
to70.0 ?m, and 44.0 to 57.5 ?m for the SSF and SFF process at 10, 15, and 20 per cent
initial solids (w/v), respectively.
A recombinant strain of Zymomonas mobilis (39679:pZB4L) was used in
saccharification following the fermentation (SFF) process varying the initial
concentration of Solka Floc. The viscosities of fermentation broth observed were in
range of 0.024 to 0.028, 0.401 to 0.058, and 0.840 to 0.087 paschal-seconds for shear
rates up to 100 reciprocal seconds at 10, 15, and 20 per cent initial solids (w/v),
respectively.
The fluid behavior of the suspensions and broth slurries in Zymomonas mobilis
ethanol fermentation was modeled using the power-law, the Herschel-Bulkley, the
Casson, and the Bingham model. The results showed that broth slurries were
pseudoplastic with a yield stress. The model slope increased and the model intercept
decreased with increasing fermentation time at shear rates normal for the fermentor.
The broth slurries exhibited Newtonian behavior at high and low shear rates during initial
SFF process.
102
INTRODUCTION
Production of fuel ethanol from lignocellulosic biomass has the potential to
reduce world dependence on petroleum while decreasing net emissions of carbon dioxide,
the principal greenhouse gas. There continues to be times, however, when ethanol
cannot compete economically with gasoline or petroleum derivatives of fossil fuels.
The opportunity therefore exists for process improvements in the conversion of biomass
to fuel alcohol that will result in more favorable production economics. High solid
loading fermentation is one such process improvement aimed at increasing both the rate
of fermentation and the final ethanol concentration and thereby reducing processing costs
(Ingledew, 1993). Positive economic advantages associated with a high-solids
saccharification process over a conventional low solids process include: lower capital
costs due to the reduced volume; lower operating costs due to less energy required for
heating and cooling; lower downstream processing costs due to higher product
concentrations; reduced disposal and treatment costs due to lower water usage
(Mohagheghi et al., 1992).
Understanding rheology of concentrated biomass slurries is important for
designing equipment and predicting process performance. Specifically, shear rate of the
flow in a mixing tank is an important parameter controlling many important industrial
processes. Fundamentally, shear rate affects processes involving mixing of Newtonian
and non-Newtonian fluids, generating/dispersing liquid/liquid droplets, and producing
fine gas bubbles for gas-to-liquid mass transfer.
Stirred tanks are usually used for the thermo-chemical fermentation. To
simulate flow of Solka Floc slurries in stirred tanks, the rheological properties of these
103
suspensions must be known. This high-solids slurry definition can be regarded as the
solids region where the slurry viscosity is highly non-Newtonian at approximately 12 to
15 per cent insoluble solids (Pimenova and Hanley, 2003). The corn stover slurries in
stirred tank reactors typically range from 10 to 40 percent solids (Ranatunga et al., 2000).
The overarching goal of this work is to investigate high-solids saccharification
following fermentation for biomass conversion using Solka Floc as the model feedstock.
The immediate objectives are to understand the high-solids SFF process, which is
expected to reduce both the risk and cost of enzyme and microorganism based process
technology. This subtask has two distinct but related efforts:
? to understand the rheology and mixing characteristics of high-solid
fermentation broths and
? to understand the performance of enzymatic cellulosic saccharification at
high solids loadings.
The primary objective of this study is to investigate the rheological behavior of
high-solids Solka Floc slurries and the particle size distribution during ethanol
fermentation, and to fit an appropriate model on the experiment data. Additionally, the
present findings can be applied to bioreactor design using computational fluid dynamics.
104
MATERIALS AND METHODS
Suspension Fluids used for Measurements
The fermentation fluids and enzyme hydrolysis suspensions used in this research
were obtained from the cultivation of Zymomonas mobilis and Spezyme CP (Novozyme
188) respectively. The composition of the culture medium, enzyme suspension, and
their reaction conditions were the same as outlined in a previous section chapter III.
Viscometer
The viscosity of the suspension at different biomass concentration was measured
by Modular Compact Rheometer Physica MCR 300 (Paar-Physica). Controlled shear-
stress measurements were done using concentric cylinder system with FL 100/6W
impeller at two different temperatures of 30, 40, and 50?C respectively. The sample
with desirable concentration was prepared and homogenized prior to measurements.
Then an appropriate volume was placed into the viscometer and was left for few minutes
to allow the temperature to stabilize. Then, rheological measurements were done three
times for each value of biomass concentration using always a fresh sample.
Concentric Cylinder System
The system consists of a stationary outer cylinder with the radius R
a
=15 mm
and a rotational inner cylinder with R
i
=10 mm. The cylinders are separated by the gap
R
a
-R
i
=5 mm into which the sample is introduced (Figure IV-1). The length of the inner
cylinder is L
c
=16 mm. By the rotation of the inner cylinder with the torque M, the
sample in the gap is sheared and the angular velocity ? is measured. Using M, ?, and
105
the geometry of the system, the shear stress ? and the shear rate
?
? can be determined
Figure IV-1. Scheme of concentric cylinder (Stirrer FL 100/6W) system of Paar
Physica modular compact rheometer (MCR 300).
Model equations:
2
ic
RL?2
M
?
???
=
?
)
R
(R
R
2
?
2
i
2
a
2
a
?
?
?
=
?
106
Measurement of Particle Size
All particle size analyses performed on the Mastersizer S (Malvern Instruments
Ltd., Malvern, U. K) using the magnetically stirred cell or the small volume sample
dispersion unit which must have a liquid phase to carry the material to be tested. The
Malvern Mastersizer-S is based on the principle of laser ensemble light scattering. It is
categorized as a non-imaging optical system, as sizing is accomplished without forming
an image of the particle on a detector. For analysis, each sample was diluted
approximately 500-fold in tap water, and the mean value between the low and high
particle sizes for a given channel were used (range: 0.01 to 1000 ?m) before being
analyzed 10 times. These results were then averaged to produce the particle size
distribution.
Calculation of Power Law Parameters
The power law parameters were calculated from concentric cylinder system with
FL 100/6W impeller data for the non-Newtonian calibration fluids for comparison
purposes. The parameters were calculated from impeller data of the Solka Floc
suspensions for comparison to published data. A linear regression analysis was
performed on the log of viscosity and log of shear rate data for the non-Newtonian fluids
and the Solka Floc slurries. The consistency index constant, K, was calculated from the
intercept of the regression analysis and the index number, n, was calculated from the
slope.
107
Yield Stress
Yield stress is defined as the shear stress that has to be applied before the material
starts to flow. Nguyen et al. (1992) indicated that the yield stress can be measured by
either direct or indirect methods. Indirect methods consist of either using rheological
models to fit the shear stress-shear rate experimental data or extrapolating the shear
stress-shear rate data to a zero shear rate. Indirect determination of the yield stress
involves extrapolation of the experimental shear stress-shear rate data to obtain the yield
value as the shear stress limit at zero rate of shear. The extrapolation is performed
numerically on the available data, or the latter can be fitted to a suitable rheological
model representing the fluid and the yield stress parameter in the model is determined.
The direct method involves shearing a fluid in a rotational viscometer at a
low and constant shear rate and measuring the shear stress as a function of time. The
stress versus time (or shear strain) response typically consists of an initially linear portion
indicating elastic solid behavior, followed by a nonlinear region, a stress overshoot, and a
stress decay region (Nguyen et al., 1992). A more convenient extrapolation technique is
to approximate the experimental data with one of the viscoelastic flow models. The
Bingham model postulates a linear relationship between ? and
?
? . However, this model
can lead to unnecessary overprediction of the yield stress. Extrapolation by means of
the nonlinear Casson model is straightforward from a linear plot of
?
2/1
versus
2/1
?
?
.
The application of the Herschel-Bulkley model is more tedious and less certain although
systematic procedures for determination of the yield value and the other model
parameters are available.
108
RESULTS AND DISCUSSION
High solids saccharification and fermentation are difficult due to the challenging
rheological characteristics of high-solid biomass slurries that can cause non-uniform heat
and mass transfer. In addition, dynamic changes in rheology and biomass properties
occur as the cellulose structure is broken down during enzymatic hydrolysis. To
overcome the poor transfer, both enzymatic hydrolysis and saccharification followed by
fermentation (SFF) process was employed at relative high solid concentration (10 to 20
per cent) using a portion loading method. The substrates were added to the reactor in
three portions (starting concentration, 5 per cent, w/v) during both reactions up to 20 per
cent final DM concentration at every four hours.
Rheological Behavior of Enzymatic Hydrolysis Suspension
Figures IV-2, IV-3 and IV-4 show the dependence of apparent viscosity of
enzyme hydrolysis suspension on biomass concentration. The graphs clearly
demonstrate a dramatic decrease of viscosity for the reloading point (i. e. initial 4 hours)
with increasing shear rate. The experimental determination of the viscosity-shear rate
and shear stress-shear rate relationships of the various formulation suspensions with
different concentrations was performed with a variable speed rotational viscometer (2 to
200 RPM).
The viscosities observed were in range of 0.0418 to 0.0144, 0.233 to 0.0348 and
0.292 to 0.0447 paschal-seconds for shear rates up to 100 reciprocal seconds and
substrate concentrations of 10, 15, and 20 per cent initial solids (w/v) measured at 50
o
C.
The fermentation viscosity and shear stress curves are depicted in Figures IV-5 and IV-6.
109
0.01
0.1
1
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
1
2
3
4
5
0 50 100 150
Shear Rate (sec
-1
)
S
h
ear S
t
r
e
s
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure IV-2. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note:
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in one portion during fermentation up to 10 percent final substrate
concentration.
(IV-2a) (IV-2b)
110
0.01
0.1
1
10
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
o
s
i
t
y (
P
a*
s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
2
4
6
8
10
0 50 100 150
Shear Rate (sec
-1
)
S
h
ea
r S
t
res
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure IV-3. Viscosity and shear stress curves as a function of shear rate for different time during initial four-hour
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in two portions during fermentation up to 15 percent final substrate
concentration.
(IV-3a)
(IV-3b)
111
0.01
0.1
1
10
100
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
2
4
6
8
10
0 50 100 150
Shear Rate (sec
-1
)
Sh
e
a
r
St
r
e
s
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure IV-4. Viscosity and shear stress curves as a function of shear rate for different time during initial four-hour enzymatic
hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in three portions during fermentation up to 20 percent final substrate
concentration.
(IV-4a) (IV-4b)
112
In this fermentation experimental results are presented for the rheological
behavior and ethanol yield of high-solids ethanol fermentation for biomass conversion
using Solka Floc as the model feedstock. A recombinant strain of Zymomonas mobilis
39679:pZB4L was used in SSF and SFF processes as a function of varying initial
concentration of Solka Floc and constant enzyme dosage. Compared with the
traditional SSF process for high solid substrate at 40
o
C, the rheological behavior in the
SFF process significantly decreased in the beginning of ethanol fermentation. The
viscosities observed were in range of 1.220 to 0.098, 3.36 to 0.133, and 5.18 to 0.192 Pa-
s for shear rates up to 100s
-1
(Figure VI-5a). On the other hand, the initial viscosities
were in range 0.024 to 0.028, 0.423 to 0.067, and 0.840 to 0.087 Pa-s for shear rates up to
100s
-1
in combined temperature (50
o
C and 30
o
C) at 10, 15, and 20 percent initial solids
(w/v), respectively (Figure IV-6a). One can see that the rheological behavior of
suspensions of fermentation broth significantly changes with its concentration and
reaction condition. At all concentration, both enzymatic suspension and fermentation
broths exhibit a pseudoplastic behavior with two Newtonian regions. However, at
relatively high solid concentration loaded by the portion method, constant viscosity was
observed, indicating only the Newtonian behavior of slurries at low and high shear rate
during initial enzymatic hydrolysis and SFF process (Figure IV-4, IV-6).
Rheological Parameter Estimation for Psudoplastic Suspension
Several researchers reported viscoelastic behavior of yeast suspensions. Labuza
113
0.01
0.1
1
10
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a*s
)
10 % Substrates
15 % Substrates
20 % Substrates
0
4
8
12
16
20
0 50 100 150
Shear Rate (sec
-1
)
S
h
e
ar
S
t
r
e
s
s
(
P
a)
10 % Substrates
15 % Substrates
20 % Substrates
Figure IV-5. Viscosity and shear stress curves as a function of shear rate for different time during initial SSF process.
Note;
1. SSF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 40
o
C, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. Substrates were added to the reactions in three portions during fermentation up to 20 percent final substrate
concentration.
3. The substrate and nutrient media were autoclaved (120?C and 20 min).
(IV-5a)
(IV-5b)
114
0.001
0.01
0.1
1
10
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a*s
)
10 % Substrates
15 % Substrates
20 % Substrates
0
2
4
6
8
10
12
14
0 50 100 150
Shear Rate (sec
-1
)
She
a
r
St
r
e
s
s
(
P
a
)
10 % Substrates
15 % Substrates
20 % Substrates
Figure IV-6. Viscosity and shear stress curves as a function of shear rate for different time during initial SFF process.
Note;
1. SFF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. Combined temperature: 50
o
C and 30
o
C.
3. Substrates were added to the reactions in three portions during fermentation up to 20 percent final substrate
concentration.
4. The substrate and nutrient media were autoclaved (120 ?C and 20 min).
(IV-6a)
(IV-6b)
115
et al. (1970) reported shear-thinning behavior of baker?s yeast (S. cerevisiae) in the range
of 1 to 100 s
-1
at yeast concentrations above 10.5 per cent (w/w). The power-law model
was successfully applied. More recently, Mancini and Moresi (2000) measured
rheology of baker?s yeast using different rheometers in the concentration range of 25 to
200 g/dm
-3
. While a Haake rotational viscometer confirmed Labuza?s results on the
pseudoplastic character of yeast suspension, dynamic stress rheometry revealed definitive
Newtonian behavior. This discrepancy was attributed to the lower sensibility of Haake
viscometer in the range of viscosity tested (1.5 to 12 mPa-s). Speers et al. (1993) used a
controlled shear-rate rheometer with a cone-and-plate system to measure viscosity of
suspensions of flocculating and nonflocculating strains of S. cerevisiae and S. uvarum.
They used the Bingham model for description of viscoelastic flow behavior of cell suspension.
The normal procedure for the estimation of the model parameters for
pseudoplastic fluids with a yield stress using rheological models employs non linear
regression of the viscometric data from concentric cylinder geometry with a numerical
package, minimizing the sum of error squares. Nonlinear fit to various data with a
statistics package (RHEOLPLUS) has sometimes given the best fit with negative yield
values, which is meaningless. So, the first point at low shear stress was not considered
in the regression analysis.
Figure IV-2b, IV-3b, and IV-6b show shear stress curves as a function of shear
rate for different times during initial enzymatic hydrolysis. The yield stress values are
116
Table IV-1. Determination of rheological parameter as function of time during initial 4-hour enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in two portions during fermentation up to 10 % final substrate
concentration.
Nomenclature;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 0.391 0.012 1.175 0.996 0.360 0.022 0.994 0.226 0.100 0.982 0.269 0.404 0.948
t= 2 hr 0.213 0.019 1.024 0.995 0.209 0.021 0.995 0.335 0.107 0.990 0.155 0.507 0.972
t= 3 hr 0.099 0.009 1.176 0.994 0.081 0.171 0.992 0.031 0.108 0.987 0.063 0.669 0.973
t= 4 hr 0.065 0.005 1.294 0.995 0.044 0.014 0.988 0.013 0.105 0.983 0.037 0.766 0.972
117
Table IV-2. Determination of rheological parameter as function of time during initial 4-hour enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in three portions during fermentation up to 15 % final substrate
concentration.
Nomenclature;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 1.191 0.060 0.987 0.998 0.763 0.071 0.931 0.759 0.1611 0.991 0.835 0.403 0.958
t= 2 hr 0.713 0.055 0.968 0.997 0.731 0.048 0.997 0.424 0.156 0.993 0.489 0.466 0.967
t= 3 hr 0.577 0.044 0.975 0.996 0.589 0.040 0.996 0.338 0.144 0.992 0.399 0.476 0.969
t= 4 hr 0.354 0.057 0.887 0.991 0.400 0.036 0.990 0.213 0.143 0.990 0.292 0.532 0.975
118
Table IV-3. Determination of rheological parameter as function of time during initial 4-hour enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions in four portions during fermentation up to 20 % final substrate
concentration.
Nomenclature;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 2.707 0.036 1.083 0.972 2.658 0.052 0.926 1.488 0.115 0.930 2.363 0.205 0.859
t= 2 hr 0.960 0.140 0.730 0.981 1.173 0.044 0.976 0.815 0.133 0.983 0.854 0.353 0.966
t= 3 hr 0.600 0.154 0.693 0.987 0.827 0.041 0.979 0.535 0.138 0.989 0.589 0.408 0.978
t= 4 hr 0.372 0.158 0.677 0.986 0.573 0.041 0.978 0.358 0.144 0.988 0.424 0.464 0.981
119
shown in Tables IV-1, IV-2, and IV-3. Shear stress-shear rate data of enzymatic
hydrolysis suspension and fermentation broth were tested for various rheological models
(Herschel-Bulkley, Bingham, Casson and power law models).
Three models (Herschel-Bulkley, Casson, and Bingham) were used to fit the
experimental data and to determine the yield stress of the slurries. Table VI-5 and VI-6
lists the results obtained for the different parameters used to fit the experimental data of
fermentation suspensions at the various concentrations. The Herschel-Bulkley model
fits the data satisfactorily over the whole experimental range at 10 to 20 per cent solids
concentration. On the other hand, Bingham and Casson equations are in excellent
agreement with result of enzymatic suspension and fermentation broth from fermentor at
10 per cent and 20 per cent respectively (Table IV-1, Table IV-3, and Table IV-5).
Figure IV-7 through IV-9 use curve fitting with an empirical rheological model as an
indirect method of determining the yield stress on a fluid. Four different models were
used to fit the behavior of fermentation broth at various concentrations: the power law
model, the Bingham model, the Casson model and the Herschel-Bulkley model. The
results of the power law model (n and K
pl
) were compared to those power law parameters
obtained with the impeller method. The Herschel-Bulkley, Bingham and the Casson
models were used to compare their yield stress results to those calculated with the direct
methods, the stress growth and impeller methods. Tables IV-4 and IV-5 show the
parameters obtained when the experimental shear stress-shear rate data for the
fermentation suspensions were fitted with all models.
120
Table IV-4. Determination of rheological parameter as function of time during initial SSF process.
Note;
1. SSF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 40
o
C, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. Substrates were added to the reactions in four portions during fermentation up to 20 % final substrate concentration.
3. The substrate and nutrient media were autoclaved (120?C and 20 min).
Nomenclature;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
% (w/v)
Substrates
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
10 % 0.787 0.906 0.474 0.982 1.931 0.092 0.973 1.378 0.198 0.994 1.622 0.357 0.970
15 % 3.269 0.052 1.134 0.989 3.139 0.093 0.989 2.407 0.173 0.961 2.700 0.264 0.879
20 % 5.516 0.044 1.257 0.980 5.175 0.132 0.948 4.107 0.197 0.941 4.597 0.235 0.848
121
Table IV-5. Determination of rheological parameter as function of time during initial SFF process.
Note;
1. SFF condition: 30 FPU/g of glucan, pH 4.8 to5.0, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. Combined temperature: 50
o
C and 30
o
C.
3. Substrates were added to the reactions in four portions during fermentation up to 20 % final substrate concentration.
4. The substrate and nutrient media were autoclaved (120?C and 20 min).
Nomenclature;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
% (w/v)
Substrates
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
10 % 0.029 0.002 1.554 0.998 0.014 0.011 0.980 0.001 0.105 0.970 0.016 0.973 0.968
15 % 0.306 0.177 0.756 0.985 0.467 0.068 0.978 0.256 0.201 0.988 0.425 0.549 0.979
20 % 0.735 0.259 0.740 0.996 1.016 0.089 0.981 0.625 0.216 0.996 0.866 0.461 0.970
122
0
1
2
3
4
0 50 100 150
Shear Rate (sec
-1
)
She
a
r
St
r
e
s
s
(
P
a)
10 % Substrates
Herschel-Bulkley
Bingham
Casson
Power Law
Figure IV-7. Comparison of the different rheological models used to fit the shear stress
as function of shear rate data of 10 per cent sold concentration of fermentation Broth at
t=0. Symbols represent experimental measurements and lines represent four different
model predictions
Note;
1. SFF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 120 rpm, Zymomonas
mobilis, strain 39679:pZB4L.
2. Combined temperature: 50
o
C and 30
o
C.
3. Substrates were added to the reactions in one portions during fermentation up
to 10 % final substrate concentration.
4. The substrate and nutrient media were autoclaved (120?C and 20 min).
123
0
2
4
6
8
0 50 100 150
Shear Rate (sec
-1
)
S
h
ear S
t
r
e
s
s
(
P
a)
15 % Substrates
Herschel-Bulkley
Bingham
Casson
Power Law
Figure IV-8. Comparison of the different rheological models used to fit the shear stress
as function of shear rate data of 15 % sold concentration of fermentation Broth at t=0.
Symbols represent experimental measurements and lines represent four different model
predictions
Note;
1. SFF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 120 rpm, Zymomonas
mobilis, strain 39679:pZB4L.
2. Combined temperature: 50
o
C and 30
o
C.
3. Substrates were added to the reactions in two portions during fermentation up
to 15 % final substrate concentration.
4. The substrate and nutrient media were autoclaved (120 ?C and 20 min).
124
0
2
4
6
8
10
12
0 50 100 150
Shear Rate (sec
-1
)
S
h
ea
r S
t
ress
(
P
a
)
20 % Substrates
Herschel-Bulkley
Bingham
Casson
Power Law
Figure IV-9. Comparison of the different rheological models used to fit the shear stress
as function of shear rate data of 20 % sold concentration of fermentation Broth at t=0.
Symbols represent experimental measurements and lines represent four different model
predictions
Note;
1. SFF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 120 rpm, Zymomonas
mobilis, strain 39679:pZB4L.
2. Combined temperature: 50
o
C and 30
o
C.
3. Substrates were added to the reactions in three portions during fermentation
up to 20 % final substrate concentration.
4. The substrate and nutrient media were autoclaved (120?C and 20 min).
125
The correlation coefficients (R
2
) between the shear rate and shear stress are 0.994
- 0.995 for the Herschel-Bulkley model, 0.988-0.994 for the Bingham, 0.982-0.990 for
the Casson model and 0.948-0.972 for the power law model for enzymatic hydrolysis at
10 per cent solid concentration (Table IV-1). The rheological parameters for Solka Floc
suspension were employed to determine if there was any relationship between the shear
rate constant, k, and the power law index flow, n. The relationship between the shear
rate constant and the index flow for fermentation broth at concentrations ranging from 10
to 20 percent is shown on Table IV-4 and IV-5. The yield
stress, consistency coefficient
and flow behavior index obtained by the FL 100/6W impeller technique decreased
significantly as function of time and concentration during enzyme reaction (Table IV-1
and IV-3) and fermentation (Table IV-4 and IV-5).
126
Determination of Particle Size Treated by Enzyme
The particle size of the solid in the suspension was determined by Mastersizer S
(Malvern Instruments Ltd., Malvern, U. K). The digested suspensions were produced in
bench scale reactors at a concentration
of 10 to 20 per cent (w/v) and temperature from 30
to 50?C for 4 and 48 hours
followed by particle size measurements at 3000 rpm. Laser
diffraction proved capable of providing rapid, reproducible results of the particle size
distribution of each sample. Ten consecutive measurements were made of each sample,
and the results averaged to produce the overall size distribution.
The particle size distribution of each slurry is illustrated in Figures IV-7, Figure IV-
8 and IV-9, which show the percentage of particles, by volume, between 0.6 and 1000 ?m.
No particles < 0.6 ?m were detected in any of the samples. The digested suspension
showed one main peak in the size range 35.6 to 48.3 ?m (Figure IV-7a, Figure IV-8a, and
Figure IV-8a). As the level of solid concentration increased, there was a shift in the
particle size distribution towards larger particles with the peak at 35.6 to 48.3 ?m
becoming less pronounced with a decrease in the number of smaller sized particles.
This is likely due to the differences in biodegradability for the different reaction
conditions.
The average particle sizes during the enzymatic treatment and fermentation process
of Solka Floc at 40 ?C and combined temperature (50 to 30?C) are given in Table IV-6.
The solids particle size distribution ranged from 57.8 to70.0 ?m for the SSF process and
from 44.0 to57.5 ?m for the SFF process at 10, 15, and 20 per cent initial solids (w/v),
respectively.
127
0
2
4
6
8
10
0 200 400 600 800
Distribution of Particle Size (Micron)
Vo
u
l
m
e
[
%
]
40 ?C
Combined Temperature (50 and 30 ?C)
Untreated Solka Floc
0
20
40
60
80
100
0 200 400 600 800
Distribution of Particle Size (M icron)
Vo
u
l
m
e
[
%
]
40 ?C
Combined Temperature (50 and 30 ?C)
Untreated Solka Floc
Figure IV-10. Percentage volume (10a) and cumulative volume particle size distribution (10b) for the substrate
during SSF and SFF process.
Note;
1. SSF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. Substrates were added to the reactions in one portion during fermentation up to 10 percent final substrate
concentration.
3. The substrate and nutrient media were autoclaved (120?C and 20 min).
(IV-10a)
(IV-10b)
128
0
2
4
6
8
10
0 200 400 600 800
Distribution of Particle Size (Micron)
Vo
l
u
m
e
[
%
]
40 ?C
Combined Temperature (50 and 30 ?C)
Untreated Solka Floc
0
20
40
60
80
100
0 200 400 600 800
Distribution of Particle Size (M icron)
Vo
l
u
m
e
[
%
]
40 ?C
Combined Temperature (50 and 30 ?C)
Untreated Solka Floc
Figure IV-11. Percentage volume (11a) and cumulative volume particle size distribution (11b) for the substrate
during SSF and SFF process.
Note;
1. SSF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. Substrates were added to the reactions in two portions during fermentation up to 15 percent final substrate
concentration.
3. The substrate and nutrient media were autoclaved (120?C and 20 min).
(IV-11a)
(IV-11b)
129
0
2
4
6
8
10
0 200 400 600 800
Distribution of Particle Size (Micron)
Vo
u
l
m
e
[
%
]
40 ?C
Combined Temperature (50 and 30 ?C)
Untreated Solka Floc
0
20
40
60
80
100
0 200 400 600 800
Distribution of Particle Size (M icron)
V
o
l
u
me
[%
]
40 ?C
Combined Temperature (50 and 30 ?C)
Untreated Solka Floc
Figure IV-12. Percentage volume (12a) and) cumulative volume particle size distribution (12b for the substrate
during SSF and SFF process.
Note;
1. SSF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. Substrates were added to the reactions in three portions during fermentation up to 20 percent final substrate
concentration.
3. The substrate and nutrient media were autoclaved (120?C and 20 min).
(IV-12a)
(IV-12b)
130
Table IV-6. Determination of rheological parameter as function of time during initial fermentation process.
Note;
Average Particle Size Range of Viscosity Viscosity at 120 rpm
% (w/v)
Substrates
SSF SFF SSF SFF SSF SFF
10 % 57.8 ?m 44.0 ?m 1.120-0.098 Pa?s 0.024-0.028 Pa?s 0.106 Pa?s 0.019 Pa?s
15 % 64.2 ?m 53.0 ?m 3.360-0.133 Pa?s 0.401-0.058 Pa?s 0.168 Pa?s 0.078 Pa?s
20 % 70.0 ?m 57.5 ?m 5.180-0.192 Pa?s 0.840-0.087 Pa?s 0.223 Pa?s 0.105 Pa?s
1. SSF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 40
o
C, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
2. SFF condition: 30 FPU/g of glucan, pH 4.8 to 5.0, 50-30
o
C, 120 rpm, Zymomonas mobilis, strain 39679:pZB4L.
3. Range of viscosity is for shear rate up to 100 s
-1
.
4. Substrates were added to the reactions in four portions during fermentation up to 20 percent final substrate
concentration.
5. The substrate and nutrient media were autoclaved (120?C and 20 min).
6.
Average particle size of untreated Solka Floc is 91.0 ?m.
131
CONCLUSIONS
The rheological analysis of high solid substrates in the bioreactor during the
enzymatic hydrolysis and ethanol fermentation showed a dramatic decrease in viscosity
as function of time and solids concentration. Initial reaction time and biomass
concentration were found to affect the bioreactor hydrodynamics. Adoption of high
solid substrates loading by portion method in the three-liter bioreactor showed significant
reduction of viscosity and a dramatic acceleration of net liquid flow appeared with
increasing biomass quantity. Rheological analysis revealed a direct dependence of
temperature and concentration of biomass in bioreactor on apparent viscosity of
fermentation broths. An increase in the level of solid concentration, with the samples less
digested, led to a shift in the size distribution, with a decrease in the number of smaller sized
particles.
For a high solid bioreactor used for ethanol production, the bioreactor should
operate below the critical biomass concentration to ensure operation in desirable flow
regime. The SFF process can be operated with relative higher solids loading up to a
maximum solids loading (20 percent w/v); however, high solids loading results in a
negative effect on transport phenomena, mixing and solid distribution in the bioreactor.
All of the information on hydrodynamics in the three-liter bioreactor can be used a priori
or during the bioprocess to optimize operational parameters in order to avoid any
occurrence of undesirable bioreactor operation to maximize the process productivity.
132
V. FLOW PATTERN SIMULATION IN A HIGH SOLID
CELLULOSE-TO-ETHANOL BIOREACTOR USING
COMPUTATIONAL FLUID DYNAMICS
ABSTRACT
The agitation system plays an important role in bioethanol production from the
Simultaneous Saccharification Fermentation (SSF) and Saccharification Followed by
Fermentation (SFF) processes. To understand and improve mixing and mass transfer in
the viscous non-Newtonian systems, flow behavior of fermentation broth was simulated
in a bench scale bioreactor (BioFlo 3000). The predictive capabilities of CFD
techniques as applied to solid-liquid stirred vessels for a high solids system are
investigated.
Based on the angular momentum balance, the required torque in mixing tanks
was calculated after the converged solution was obtained as percent of solid suspension.
The simulated power number in the turbulent regime was 5.0 for Rushton turbine
impeller. The Reynolds number was 248.6, 64.5, and 50.3 at 10, 15, and 20 per cent
initial solids (w/v), respectively.
The simulation of the mixed bioreactor was conducted with a baffled Rushton
turbine impeller operating in laminar regime. The standard k-? turbulent model was
133
employed in the mixing tank simulation with a Rushton turbine impeller. Fluid flow
turbulent kinetic energy and turbulent dissipation rates in the three liter reactor have been
simulated as a function of solid concentration. The result visually and analytically
showed that slow or stagnant flow regions exist that could result in poor nutrient, gas, and
heat transfer between top impellers and bottom of the tank.
134
INTRODUCTION
Computational fluid dynamics (CFD) is the science of predicting fluid flow, heat
transfer, mass transfer, chemical reactions, and related phenomena by solving the
mathematical equations which govern these processes using a numerical process (Kuipers
and van Swaaij, 1997; Van den Akker, 1997; Sundaresan, 2000; Ranade, 2002). The
result of CFD analyses is relevant engineering data used in conceptual studies of new
designs, detailed product development, troubleshooting and redesign. This research
addresses the simulation of various high solid suspensions in stirred tanks to determine
apparatus performance in bioreactors.
A number of modeling techniques have been proposed and implemented in
commercial codes, and the choice of techniques is not always straightforward for the
normal user. Moreover, the consistency of the simulation predictions with the actual
flow field and energy distribution has been demonstrated only in a few cases. Therefore,
there is still a need for further analysis and development of the modeling techniques and
of comparison of the simulation results with experimental data.
The economic of ethanol production are such that the cost of the power required
to agitate the vessel is critical to the overall profitability of the enterprise. In the mixing
process, prediction of the power or torque draw by the impeller, the flow pattern, and
flow regime involved at different suspension concentration is the important information
in design and operation to reach the desired process result.
In the present work computational analyses were performed for baffled tanks agitated
with two Rushton impellers. The suspension of Solka Floc of different diameters
135
and various concentrations up to 20 per cent (w/v) were studied. Here, the Multiple
Reference Frame and standard k-? model for viscose flows available in commercial CFD
codes have been tested, coupled with fully predictive solution of the transport of
suspension in the process vessel.
The purpose of this research was to simulate the flow of a three-liter fermentor
before designing a full scale high-solids fermentation. This simulation provides a
starting point for identifying slow, stagnant, or recirculation zones where nutrient and gas
starvation of cells could potentially occur. The commercially available programs
Mixsim 2.1.10 ? and Fluent 6.2.20 ? were used to discretize the equation of continuity
and motion of the relative high solid suspensions in stirred tanks. Fulfillment of these
objectives will assist in better understanding of the ethanol production in the chemical
mixing process and could allow fermentor deign engineer to predict the degree of flow
for the full scale fermentor.
136
MATERIALS AND METHODS
Tank Geometric Configuration of the Investigated Vessel
Figure V-1. NBS 3 L Bioreactor Tank Geometry: Diameter=138mm, Liquid
Height=177mm, Bottom shape=ellipse, Working volume=2 L, Media= High
Concentrated Solka Floc, Baffle= 4, Rotational speed: 120 rpm, Impeller Number= 2,
Impeller Type= Radial disk, Number of blades= 6.
137
The k-? Mathematical Models
The basic two transport equations that need to be solved for this model are for the
kinetic energy turbulence, k, and the rate of dissipation of turbulence, ? (FLUENT,
2001):
??
?
?
??
?
?+
?
?
?
?
?
?
?
?
?
?
+
?
?
=
?
?
+
?
?
k
ik
t
i
i
i
G
x
k
x
kU
xt
k
)(
)(
(V-1)
k
CG
k
C
x
t
x
U
xt
k
ii
i
i
2
21
)(
)( ?
?
??
??
?
???
??
++
?
?
?
?
?
?
?
?
+
?
?
=
?
?
+
?
?
(V-2)
The quantities C1, C2, ?
k
, and ?
?
are empirical constants. The quantity G
k
appearing in both equations is a generation term for turbulence. It contains products of
velocity gradients, and also depends on the turbulent viscosity:
i
j
i
j
j
i
tk
x
U
x
U
x
U
G
?
?
?
?
?
?
?
?
?
?
?
?
+
?
?
=?
(V-3)
Other source terms can be added to equation (V-1) and (V-2) to include other physical
effects such as swirl, buoyancy or compressibility, for example. The turbulent viscosity
is derived from both k and ?, and involves a constant taken form experimental data, C
?
,
which has a value 0.09:
?
???
2
k
C
t
=
(V-4)
To summarize, the solution process for the k-? model, transport equations are solved for
the turbulent kinetic energy and dissipation rate.
138
Constant N
p
and P per Liquid Volume
b
bb
N
D
H
aP
?
?
?
?
?
?
?
?
?
?
?
?
=
62.0
(V-5)
?
?ND
N
2
Re
=
(V-6)
?
53
DN
P
N
p
=
(V-7)
N
Re
: Reynolds number
a, b : constant value (radical disk impeller, a=5, b=0.8)
N
p
=power number, ratio of applied force to mass times acceleration
P=power input (W)
?=density of liquid (Kg/m
3
)
N=impeller speed (rpm)
N
b
=impeller blade number
H
b
=disk height (m)
D=impeller outer diameter (m)
Simulation Model
- Type: 3D cylindrical
- Analysis model: Multiple Reference Frame (MRF)
- Turbulent model: Standard k-? model
Simulation Tool Package
The simulations were performed using software package:
- Mixsim V. 2.1.10 - FLUENT V. 6.2.20
on a super computer at the University of Louisville.
139
Description of Supercomputer
Adelie is a Linux-based computational cluster with two master login nodes, 17
dual-processor, dual core Opteron compute nodes and six dual-Opteron compute nodes.
The system has approximately 100 GB of RAM and 5.1 terabytes of external disk storage
in a RAID 5 array managed by dual NAS heads with active failover and dual fiber
channel NAS controllers for high availability. Each node also has additional local drive
space. Backups are to an attached tape drive. The OS is SUSE Linux.
140
RESULTS AND DISCUSSION
Vessel Geometry and Grid Generation
Figure V-2. Nomenclature used to describe the mixing system
The configuration of the physical model for simulating a mixing tank with
Rushton impeller consists of a ellipsoidal cylindrical tank with four equally spaced wall
mounted baffles extending form the vessel bottom to the free surface, stirred by a
centrally-located six-blade Rushton turbine impeller. The tank diameter measured 0.138
meters, and baffle width was 0.008 meter. The impeller diameter was 0.046 m (D/T=3)
for all impellers. The distance between the impellers was 0.061 m. The impeller
center was positioned at a distance C=T/3 off the tank bottom. The liquid level was
equal to the tank diameter, Z/T=1.3.
141
The suspension was fermentation broth with various viscosities. The impeller was
mounted on a 0.0025 meter diameter shaft rotating at 120 rpm corresponding to a range
of Reynolds number of 50 to 300??in both the down- and up-pumping modes.
The commercial mesh generator Mixsim 2.1.10 was used to create a structured,
non-uniform multi-block grid, as shown in Figure V-3, with inner and outer zones by an
interface in order to enable the use of multiple reference frame techniques. The wide
nature of the impeller blades relative to the diameter of the hub results in the overlapping
of blades close to the hub. Consequently, simulation of only part of the vessel in order
to decrease computational expense was not possible and therefore it was necessary to
model the entire vessel geometry.
142
Convergence Criteria and Blend Time
Simulations were typically considered converged when the scaled residuals
(continuity, X, Y, Z-velocity, k, and?), normalized relative to the maximum circulating
flow, fell below 6E-04 by iteration 5,000. Further checks for convergence were made
by verifying that global quantities, such as the power number, and the circulation number,
were constant.
The model predictions are compared with the results of the experimental blend
time correlation. MixSim can computed the blend time for a single impeller in a tank,
as well as the effective blend time for a tank with multiple impellers.
The blend time to achieve 99 per cent uniformity in a tank with a multiple
impellers is computed from (FLUENT, 2006).
effm
k
t
,
605.4
99
=
where a sum over all impellers (i =1 to n) is performed to computer k
m,eff
:
5.0
1
,,
?
?
?
?
?
?
?
?
?
?
?
?
==
??
=
Z
T
T
D
Nakk
i
b
i
n
i
iiimeffm
All of the graphs show that for a uniformity above 99 % at t=13.37s. And, all calculations
were steady state.
143
Grid Refinement
The geometry was defined in the Cartesian (x, y, z) coordinate system. After the
grid is generated, the skewness of 97.41 percent cells was below 0.6. It is very
important to assess the quality of the grid, because properties such as skewness can
greatly affect the accuracy and robustness of the CFD solution. In general, high-quality
meshes contain elements that possess average Q values of 0.4. Even a single cell with
skewness > 0.98 may destroy convergence in the whole computation. The detailed
histogram of skewness of this simulation was in Appendix C.
The computational grid was defined by 570,000 unstructured, nonuniformly-
distributed, 182,000 nodes, and tetrahedral cells. When refining the mesh, care was
taken to put most additional mesh points in the regions of high gradient around the blades
and discharge region.
144
Figure V-3
145
Figure V-4
146
Figure V-5
147
Panel 1
(x=0)
Panel 2
(z=0.143 m)
Panel 3
(z=0.113 m)
Figure V-6. Sweep surface in mixing tank
Panel 4
(z=0.089 m)
Panel 5
(z=0.053 m)
Panel 6
(z=0.035 m)
Panel 7
(z=0.005 m)
148
Power Law Flow Behavior Index (0.46 ? n? 0.97)
The viscous fermentation broth used in this projects exhibited pseudoplastic
rheology that is modeled quite well over a wide range of shear rates by the power law
model. Consequently the power law was used to model fluid rheology in this study,
with 0.0192, 0.0775, and 0.105 paschal-second at 10, 15, 20 per cent concentration
respectively. The upper and lower limits for n in this study were obtained from
experiments that n values for viscous fermentation broth (Zm. mobilis cultures) are in the
range 0.46 to 0.97 during the portion batch fermentation.
Turbulence in a Tank with Baffled Rushton Impeller
The blade predicted tip velocity V
tip
at the rotational speed of 120 RPM was 0.29
m/s (Figure V-17), and an impeller Reynolds number based on tip speed and impeller
diameter was 248.6, 64.5, and 50.3 at 10, 15, and 20 per cent solid concentration
respectively. The resulting simulation was in the laminar flow regime (Re = ?.N.D
2
/?,.
ranging from Re = 50 to Re = 300). As the impeller Reynolds number decreased, a
transition to radial flow occurs. At impeller Reynolds numbers less than 100, strictly
radial flow is observed.
The standard k-? turbulent model was employed to treat the strong swirling flow
induced by Rushton impeller. Figure V-7, V-14 and V-21 shows the predicted flow
pattern for panel 1 in the mixing tank with Rushton turbine impeller respectively. The
maximum and minimum velocity magnitudes were slightly different. This slight
difference may be caused by different viscosity and solid concentration.
149
Figure V-7
150
Figure V-8
151
Figure V-9
152
Figure V-10
153
Figure V-11
154
Figure V-12
155
Figure V-13
156
Predicted Velocity Distribution
Figures V-7, V-14, and V-21 show the three types of steady-state flow patterns
observed with the MFR models at the vertical baffle plane (tank-cut-plane 90
o
, panel 1)
at 10, 15, and 20 per cent solids concentration, respectively. The simulation conditions
used to generate each of the flow fields are listed below each figure. The magnitude of
the velocity at any point in the flow field is indicated by the length and color of the arrow
at that point.
A general pattern can be described as follows: a strong flow is found right at the
two impellers pushing the fluid downward at an angle of about 45
o
for all of the
concentrated suspensions. An upward flow can be found below the two impeller, along
the tank wall between the two impellers, and right off of the tip of the impellers that in
turns causes some circulation. Other circulation areas can be seen around the bottom of
the tank. Some of the upward flow caused by the lower impeller is drawn back to the
lower impeller, while some follows the center path upward to the upper impeller. Some
of the flow pumped down by the upper impellers is drawn even further down by the
lower impeller, with some other circulating back to the top. Little flow occurs in the
region the fermentor wall. This primary circulation pattern is completed as the liquid
re-enters the impeller region at the top impeller. As a result of this flow pattern, there is
virtually no movement of fluid between gas-liquid interface and the top impeller.
The three plots generally show similar flow patterns with a strong primary
circulation loop in the lower half of the tank and a smaller secondary loop below the
impeller. Here in the discharge jet, there are not great differences the predicted
157
solution between 10 per cent and 20 per cent around the impellers. This explains the
portion loading of substrates could reduce viscosity for the high solid fermentation
compared to traditional high substrate loading.
Axial Velocity
The contour of the axial velocity profile between tank bottom and fermentor wall
is highly dependent on the value of the flow behavior index (n). As expected, higher
values of n (n= 0.97) produce a more parabolic profile, whereas low n values (n=0.46)
produce a more blunt profile. For most of the conditions tested for this study, the
circumferential averaging axial velocity are plotted for the different solid concentration
as function of tank radial position on the bottom panel of the mixing tank in Figure V-27
though V-31. The average velocities computed in the study varied from 0 to 0.003 m/s
on the panel 7 (Figure V-31). Compared to water data, the suspension velocities were
smaller..
Figures V-10 through V-16 and V-23 are contour plots derived from the quadratic
model, showing the effects of impeller speed (120 rpm) on average velocity (m/s) in this
region for three different concentration suspensions. From the contour plots, it is
evident that the maximum values of V
tip
(0.051V
tip
for 10 percent, 0.046V
tip
for 15 percent,
0.045V
tip
for 20 percent) are found below the agitator. The maximum V
tip
increases as n
increases.
On the other hand, a relatively weak upward flow was found near the center
bottom of the tank and below the baffle, creating the circulation region. Specifically,
the
158
reverse swirling suspension has been measured in a small region in the top of the upper
impeller and in the corner of baffles with a minimal velocity (?0.049 V
tip ,
-0.048 V
tip
, and
-0.049 V
tip
). These results imply that near the center bottom of the tank, the fluid axial
velocities of the fluid were not uniform, perhaps resulting in the solid suspension staying
on the bottom of tank. These phenomena were more significant as solid concentration
increased.
159
Figure V-14
160
Figure V-15
161
Figure V-16
162
Figure V-17
163
Figure V-18
164
Figure V-19
165
Figure V-20
166
Stagnant and Slow Flow Zones
From the contour plots, conditions promoting essentially stagnant flow can be
identified when the average velocity is on or below the 0 m/s contour color. Of course,
if anaerobic fermentation broth between the corner of baffles and the outer wall was
completely stagnant, the cells would quickly become starved of nitrogen, nutrient, and
stop synthesizing product. The model indicates that, with n = 0.96, 0.55, and 0.46, flow
up the under baffle region can be stagnant at the impeller speed 120 rpm and the distance
between tank bottom and the baffles is 0.05 m.
In summary, it seems that, in addition to there being a potential for nitrogen
nutrition starvation in the upper portion of the baffle-wall region when flow through this
region is slow, there is also a potential for stagnant flow and nitrogen and nutrition
starvation in the region between the top impeller and the gas-liquid interface when flow
through the fermentor wall region is slow. In real fermentations, there is some surface
aeration near the gas-liquid interface. As expected, the simulations with higher values
of n exhibited larger low flow space.
167
Figure V-21
168
Figure V-22
169
Figure V-23
170
Figure V-24
171
Figure V-25
172
Figure V-26
173
Shear Stress and Turbulent Viscosity in Mixing Tank
An understanding of the velocity flow fields is a prerequisite to understanding
mixing and key physical parameters such as shear stress, flow fluctuations, and vorticity
fields. The circumferential averaging shear stresses are plotted for the different solid
concentration as function of tank radial position on three different panels by z direction of
the mixing tanking in Figures V-32 through V-35. The fluid suspension near the blade
wall is accelerated by an imbalance of shear forces. The average maximum values
determined by circumferential averaging model on the middle of tank were (0.008 ?
avg
for
10 per cent, 0.025?
avg
for 15 per cent, and 0.035?
avg
for 20 per cent) near the impellers.
Figures V-13, 20, and 26 show the contour of the distribution of the turbulent
viscosity, modeled by three different flow behavior indexes. The maximum viscosity
were found in the midpoint (z=0.09m) of the tank -0.084 paschal-seconds for 10 per cent,
0.050 paschal-seconds for 15 per cent, 0.039 paschal-seconds for 20 per cent. With
higher values of n, the fluid viscosity was less affected by shear and the fluid encountered
a resistance that significantly impeded flow in the wall region.
Results for average shear stress and contour distributions of viscosity over the
range of tank radial position in the mixing tank illustrated that the fluid viscosity was
significantly reduced in the high shear stress regions. Consequently, the fluid
encountered little resistance as it moved rapidly through this region.
174
Turbulence Kinetic Energy and Dissipation Rate
The distribution of turbulent kinetic energy and dissipation rates as shown in
Figures V-11, V-18 and V-24 and Figures V-12, V-19 and V-25 are characteristic of the
reactor geometry. Specifically, these turbulent dissipation rates have been used to
obtain the local shear rates for calculating the fermentation broth viscosity. The
turbulent k and ? predicted by the various viscosity suspension with the maximum values
(k=0.022V
tip
2
for 10 per cent, k=0.051V
tip
2
for fifteen per cent k=0.059V
tip
2
for 20 per
cent) and found in the discharge region and a surrounding zone of relatively high
turbulent kinetic energy.
The circumferential averaging k and ? are plotted for the different solid
concentration as function of tank radial on panels by x=0 of the mixing tank in Figures V-
39 through V-41. As expected, relatively high dissipation rates were found near the
impellers. The values of k are close to zero with low dissipation rates elsewhere.
175
-5.00E-03
0.00E+00
5.00E-03
1.00E-02
1.50E-02
2.00E-02
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
Z
-v
e
lo
c
ity
(
m
/s
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
-4.00E-02
-3.00E-02
-2.00E-02
-1.00E-02
0.00E+00
1.00E-02
2.00E-02
3.00E-02
4.00E-02
5.00E-02
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
Z
-
v
e
lo
c
ity
(
m
/s
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-27. Average of axial velocity of 2 L suspension as tank radial at panel 1.
Figure V-28. Average of axial velocity of 2 L suspension as tank radial at panel 2.
176
-4.00E-02
-3.00E-02
-2.00E-02
-1.00E-02
0.00E+00
1.00E-02
2.00E-02
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
Z
-
v
e
l
o
c
i
ty
(m
/s
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
-4.00E-02
-3.00E-02
-2.00E-02
-1.00E-02
0.00E+00
1.00E-02
2.00E-02
3.00E-02
4.00E-02
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
Z
-
v
e
l
o
c
i
ty
(m
/
s
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-29. Average of axial velocity of 2 L suspension as tank radial at panel 4.
Figure V-30. Average of axial velocity of 2 L suspension as tank radial at panel 6.
177
-6.00E-03
-4.00E-03
-2.00E-03
0.00E+00
2.00E-03
4.00E-03
6.00E-03
8.00E-03
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
She
a
r
St
r
e
s
s
(
P
a
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
-1.00E-02
-5.00E-03
0.00E+00
5.00E-03
1.00E-02
1.50E-02
2.00E-02
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035
Position as Tank Radial (m)
Z
-
v
e
l
o
c
i
ty
(m/s
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-31. Average of axial velocity of 2 L suspension as tank radial at panel 7.
Figure V-32. Average of shear stress of 2 L suspension as tank radial at panel 1.
178
-5.20E-02
-2.00E-03
4.80E-02
9.80E-02
1.48E-01
1.98E-01
2.48E-01
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
She
a
r
St
r
e
s
s
(
P
a
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
-4.00E-02
-3.00E-02
-2.00E-02
-1.00E-02
0.00E+00
1.00E-02
2.00E-02
3.00E-02
4.00E-02
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
S
h
ear S
t
r
es
s
(
P
a)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-33. Average of shear stress of 2 L suspension as tank radial at panel 3.
Figure V-34. Average of shear stress of 2 L suspension as tank radial at panel 4.
179
-5.00E-02
0.00E+00
5.00E-02
1.00E-01
1.50E-01
2.00E-01
2.50E-01
3.00E-01
3.50E-01
4.00E-01
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
S
h
e
ar S
t
res
s
(
P
a)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
0.00E+00
1.00E-03
2.00E-03
3.00E-03
4.00E-03
5.00E-03
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
T
u
rb
u
l
e
n
t
Ki
n
e
t
i
c E
n
erg
y
(
m
2
/
s
2
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-35. Average of shear stress of 2 L suspension as tank radial at panel 5.
Figure V-36. Average of turbulent kinetic energy (k) of 2 L suspension as tank
radial at panel 1.
180
0.00E+00
2.00E-03
4.00E-03
6.00E-03
8.00E-03
1.00E-02
1.20E-02
1.40E-02
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
T
u
rb
u
l
en
t
Ki
n
e
t
i
c E
n
erg
y
(
m
2
/
s
2
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
0.00E+00
2.00E-03
4.00E-03
6.00E-03
8.00E-03
1.00E-02
1.20E-02
1.40E-02
1.60E-02
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
T
u
r
bule
n
t
K
i
ne
t
i
c
E
n
e
r
g
y
(
m
2
/
s
2
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-37. Average of turbulent kinetic energy (k) of 2 L suspension as tank
radial at panel 3.
Figure V-38. Average of turbulent kinetic energy (k) of 2 L suspension as tank
radial at panel 5.
181
0.00E+00
5.00E-01
1.00E+00
1.50E+00
2.00E+00
2.50E+00
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
T
u
r
b
u
l
e
n
t
Di
ssi
p
a
t
i
o
n
Ra
t
e
(
m
2
/
s3
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
0.00E+00
2.00E-01
4.00E-01
6.00E-01
8.00E-01
1.00E+00
0 0.01 0.02 0.03 0.04 0.05 0.06 0.07
Position as Tank Radial (m)
T
u
r
b
ule
nt
D
is
s
ip
a
t
io
n
R
a
t
e
(
m
2
/s
3
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-39. Average of turbulent dissipation rate (?) of 2 L suspension as tank
radial at panel 1.
Figure V-40. Average of turbulent dissipation rate (?) of 2 L suspension as tank
radial at panel 3.
182
0.00E+00
5.00E-01
1.00E+00
1.50E+00
2.00E+00
2.50E+00
3.00E+00
0 0.10.020.030.40.050.060.07
Position as Tank Radial (m)
Tu
r
b
u
l
e
n
t D
i
s
s
i
p
a
ti
o
n
R
a
te
(
m
2
/
s3
)
10 % (w/v) Suspension
15 % (w/v) Suspension
20 % (w/v) Suspension
Water
Figure V-41. Average of turbulent kinetic energy (k) of 2 L suspension as tank
radial at panel 5.
183
CONCLUSIONS
Flow pattern calculations for potential operating conditions of multiple Rushton
six blade agitators in the ellipsoidal bottom tank have been performed to assess mixing
behavior. Velocity and shear stress criteria were developed to assess the ability of
liquid flow to lift and suspend solids deposited on the bottom surface of the tank. The
modeling results will help determine acceptable agitator speeds and tank liquid levels to
ensure
suspension of solid particles deposited during high solid fermentation.
A few important observations with regard to the effect of fluid viscosity on
fermentation suspension in the laminar flow regime have been made in this work. The
main interest was axial and mixed-flow pattern of the two impellers since they are the
most important considered for viscous suspension mixing. It was found that a various
Reynolds numbers, the axial flow component for these impellers was suppressed on the
bottom of the tank, such that overall flow was predominantly radial. Specifically, this
relatively weak distribution of axial velocities at the bottom of the tank may cause the
solid particles to stay around the bottom of the tank. This condition becomes more
significant with increased solid concentration.
The simulation shows that there is a potential for slow flow or stagnant fluid
between the bottom of tank and the fermentor wall and also above the top impeller. In
an aerobic fermentation, both of these regions could become depleted of oxygen. High
shear rates and energy dissipation rates could be found near both impellers. In all of
fermentations, high shear and energy dissipation regions could deactivate the
microorganism.
184
Viscosity fields suggest a relationship between primary flow pattern and the location of
high viscosity (low mass transfer) regions. These results suggest that correlations for
determining the overall heat transfer coefficient in stirred tanks may need to be modified
for viscous fluids.
A CFD package such as FLUENT can be used to provide valuable insight into the
relationship between fermentor configuration and flow. The results of such studies
should prove of interest especially to engineers who are concerned with bulk mixing,
mass transfer and heat transfer in large fermentor with viscous non-Newtonian fluids.
185
VI. OVERALL CONCLUSIONS AND RECOMMENDATIONS
Conclusions
The following conclusions could by drawn form the experiments:
1. In baffled tanks with Ruston impellers, a better concentration distribution
throughout the tank and therefore improvement in the mixing efficiency is
achieved to yield high glucose conversion at high solid substrates. Applying a
prehydrolysis at combined temperature (50 -30
o
C) in a portion loading (30 FPU/ g
cellulose), the final solid concentration could be increased up to 20 per cent DM
concentration. The SFF process even at relative high cellulose l0ading resulted
in a remarkable ethanol yield (83.6, 73.4, and 21.8 per cent at 10, 15, and 20 solid
per cent, respectively).
2. Three models (Herschel-Bulkley, Casson, and Bingham) were used to fit the
experimental data and to determine the yield stress of the slurries. The Herschel-
Bulkley model fits the data satisfactorily over the whole experimental range at 10-
20 per cent solid concentration. The range of R
2
(squared multiple correlation
coefficients) for Herschel-Bulkley model was 0.985 to 0.998. In addition ,
Bingham and Casson equations are in excellent agreement with result of
enzymatic suspension and fermentation broths at 10 per cent and 20 per cent.
186
3. Flow pattern calculations for potential operating conditions of a Rushton six blade
agitator in the ellipsoidal bottom tank (BioFlo 3000) have been performed to
assess mixing behavior. Velocity and shear stress criteria were developed to
assess the ability of liquid flow to lift and suspend solids deposited on the bottom
surface of the tank. The modeling results will help determine acceptable agitator
speeds and tank liquid levels to ensure suspension of solid particles deposited
during precipitation operations.
4. Fermentation broths at three different viscosities were evaluated: 0.0192, 0.0775,
0.105 kg/m?s. at 10, 15, and 20 per cent respectively. A three-dimensional CFD
approach was used with a two-equation turbulence model and multiple reference
frames. Free surface motion was assumed to be negligible compared to forced
convective motion for the operating conditions evaluated. The top liquid surface
was assumed to be stationary at atmospheric pressure. No-slip boundary
conditions were used at the blade surface and the tank walls. Rotational motion
of the agitator was simulated by using a rotating reference frame with respect to
the adjacent fluid media.
Recommendations
This research used process engineering methods to optimize the bioprocess for
bioethanol production; however, this process offers numerous additional challenges that
need to be studied in more details in order to provide better understanding of the
fermentation and simulation processes.
187
1. In the high solids fermentation, besides the carbon sources, all other compounds
such as nitrogen sources and inhibitory compounds should be clearly analyzed.
Where inhibitory compounds are found, pretreatment may be required to remove
these substances before use in the fermentation. In addition, the nitrogen source,
which was not sufficiently present in the suspension, could be the key for
determining the metabolism of lignocellulosic substrate by Zymomonas mobilis.
2. In long-term fermentations of mixed high carbon substrates (glucose/xylose), a
portion substrate loading is necessary to maintain fungal activity and enhance
ethanol fermentation. In an anaerobic reaction in which the bacterium Z. mobilis
uses glucose to form ethanol and carbon dioxide, small amounts of acetate and
glycerine are produced. If oxygen is introduced to the reaction, the production
of acetate and glycerine will increase, thus decreasing the purity of the desired
ethanol product. Therefore, it is important to determine the fermentation mode
(aerobic or anaerobic) involved in portion loading during the fermentation. By
comparing ethanol yield and its quality under the fermentation modes, the optimal
feeding strategy and fermentation can be clearly determined in the pilot scale.
3. Future work will be aimed at increasing the productivity and reducing inhibition
by studying continuous operation at combined temperature. Figure VI-1 shows a
schematic diagram of continuous saccharification fermentation process in the
pilot plant scale. Continuous operation with a series of reactors fed with
partially digested biomass withdrawn from the previous tank would greatly
benefit the economy of the process by minimizing the time spent on reactor
startups and terminations and by overcoming of incompatible temperature
188
requirements during SSF process. Once validated, the CFD method will be then
applied to the design of a full-scale reactor that is to be constructed without
additional experiments. As fundamental equations of conservation are used, the
method is readily applied to any new geometry.
4. Future simulation work will be focused on increasing the axial velocity and
reducing stagnant zone around bottom of tank in high solid fermentation by
modifying geometry (Figure VI-2). By simulation for the new geometry, the
optimal flow pattern can be clearly determined in the pilot scale.
189
Figure VI-1. Schematic diagram of continuous saccharification and fermentation at
separate temperature condition.
Parallel series saccharification at 50
o
C
keeping concentration over 15 percent
Ethanol fermentation at 30
o
C keeping
concentration over 15 percent
Supplying
sugar
Ethanol Production
(over 98 % purity)
Recycled production
(Cell biomass)
190
Figure VI-2. Modified existing bioreactor for improving axial velocity.
[Add bottom shaft mounted Lightnin A200]
Impeller: Lightnin A200
Diameter: 0.03m
Axial Location: 0.013 m
191
BIBLIOGRAPHY
Allen, D. G., and Robinson, C. W., 1990. Measurement of Rheological Properties of
Filamentous Fermentation Broths, Chem. Eng. Sci., 45(1), 37-48.
Amanullah, A., Christensen, L. H., Hansen, K., Nienow, A. W., and Thomas, C. R., 2002.
Dependence of morphology on agitation intensity in fed-batch cultures of
Aspergillus oryzae and its implications for recombinant protein production,
Biotechnology and Bioengineering, 77, 815-826.
Andrew, S. P. S., 1982. Gas-Liquid Mass Transfer in Microbiological Reactors, Trans
AIChE J., 60, 3-13.
Bailey, J. E., and David, F. O., 1986. 2
nd
ed., Biochemical Engineering Fundamentals.
McGraw-Hill, New York.
Bakker, A., Fasano, J. B., and Myers, K. J., 1994. Effects of flow pattern on solid
distribution in a stirred tank, I. Chem. E. Symp. Ser., 136, 1-8. IChemE, Rugby.
Bakker, A., and Gates, L. E., 1995. Properly Choose Mechanical Agitators for Viscous
Liquids, Chem. Eng. Prog., 25-34.
Barrue, H., Bertrand, J., Cristol, B., and Xuereb, C., 1999. Eulerian simulation of dense
solid-liquid suspension in multi-stage stirred vessel, Proc. 3
rd
Int. Symp. Mixing;
Ind. Process, Osaka, 37-44 (Society of Chemical Engineers: Tokyo, Japan).
B?guin, P., and Aubert, J. P., 1994. The biological degradation of cellulose, FEMS
Microbiol. Rev., 13, 25-58.
Bird, R. B., Stewart, W. E., and Lightfoot, E. N., 2002. Transport Phenomena, John
Wiley & Sons, Inc.
Bisaria, V. S., 1991. Bioprocessing of Agro-residence to glucose and chemicals. In:
Martin, A. M. (Ed.), Bioconversion of waste materials to industrial products,
Elsevier Applied Science, London, New York, 210-213.
Blotkamp, P. J., Takagi, M., and Pemberton, M. S., 1978. Enzymatic hydrolysis of
cellulose and simultaneous fermentation to alcohol, Biochemical Engineering:
Renewable Source of Energy and Chemical Feedstocks, AIChE Symp. Series,
74, No. 181, 85-90.
192
Bongenaar, J., Kossen, N., Metz, B., and Meijboom, F., 1973. A Method for
Characterizing the Rheological Properties of Viscous Fermentation Broths,
Biotechnol. Bioeng., 15, 201-206.
Brito-de la Fuente, E., Leuliet, J. C., Cholpin, L. and Tanguy, P. A., 1992. On the
Effect of Shear-Thinning Behavior on Mixing with a Helical Ribbon Impeller,
Process Mixing: Chemical and Biochemical Applications, 88, 28-31.
Capital Press Agriculture Weekly, June 05, 2005. Biomass: Our future energy source.
Carson, Z., 2005. Alternative Fuels as a Solution: History of Alternative Fuel
Development, .
Chang, M. M., Chou, C., and Tsao, G. T., 1981. Structure, Pretreatment and Hydrolysis
of Cellulose. Adv. Indus. Biochem. Eng., 20, 15-42.
Charles, M., 1978. Technical Aspects of the Rheological Properties of Microbiological
Cultures, Adv. Biochem. Eng., 8, 1-62.
Chum, H. L., Douglas, L. J., Feinberg, D. A., and Schroeder, H. A., 1985. In Evaluation
of Pretreatments of Biomass for Enzymatic Hydrolysis of Cellulose, SERI/TP-
231-2183; Solar Energy Research Institute: Golden, CO.
Cheung, S. W., and Anderson, B. C., 1997. Laboratory Investigation of Ethanol
Production from Municipal Primary Wastewater Solids, Bioresource. Technol.,
59, 81-96.
Christakopoulos, P., Macris, J. B., and Kekos, D., 1990. On the mechanism of direct
conversion of cellulose to ethanol by Fusarium oxysorum: Effect of cellulose
and ?-glucosidase. Appl. Microbiol. Biotechnol., 33, 18-20.
Converse, A. O., Ooshima, H., and Burns, D. S., 1990. Kinetics of Enzymatic
Hydrolysis of Lignocellulosic Materials Based on Surface Area of Cellulose
Accessible to Enzyme and Enzyme Adsorption on Lignin and Cellulose. Appl.
Biochem. Biotechnol., 24-25:67-73.
Davis, M., Baker, J. O., Rignall, T., and Himmel, M. E., 2002. Changes in Cellulose
Morphology of Pretreated Yellow Poplar during Enzymatic Hydrolysis. NREL
Report No. PO-510-32125.
Decker, S., Sommerfeld, M., 1996. Calculation of particles suspension in agitated
vessels with the Euler-Lagrange approach, IChemE Symp. Ser., 140, 71-82,
IChemE, Rugby.
193
Derksen, J. J., 2003. Numerical simulation of solids suspension in a stirred tank. AIChE
J., 49, 2700-2714.
Dronawat, S. N., Rieth, T. C., Svihla, C. K., and Hanley, T. R., 1996. Use of a Helical
Impeller to Determine Steady Shear Characteristics of Filamentous Suspensions,
Proceedings of the 5
th
World Congress of Chemical Engineering, 1, 629.
Duff, S. J. B., and Muray, W. D., 1996. Bioconversion of forest products industry waste
cellulosics to fuel ethanol: a review, Bioresource Technol., 55, 1-33.
Efficiency and Conservation Authority (EECA), 2005. Renewable Energy: Improving
Energy Choice 8.
Eriksson, T., Karlsson, J., and Tjerneld, F., 2002. A Model Explaining Declining Rate
in Hydrolysis of Lignocellulosic Substrates with Cellobiohydrolase I (Cel7A) and
Endoglucanase I (Cel7B) of Trichoderma reesei, Appl. Biochem. Biotechnol.,
101:41-59.
Fan, L. T., Gharpuray, M. M., and Lee, Y. H., 1987. Cellulose Hydrolysis. Springer-
Verlag, Berlin.
Fan, L. T. and Lee, Y. H., 1983. Kinetic Studies of Enzymatic Hydrolysis of Insoluble
Cellulose: Derivation of a Mechanistic Kinetic Model. Biotechnol. Bioeng., 24,
2707-2733.
Fein, J. E., Potts, D., Good, D., Beavan, M., O?Boyle, A., Dahlgren, D., Beck, M. J., and
Griffith, R.L., 1991. Development of an Optimal Wood-to-Fuel Ethanol
Process Utilizing Best Available Technology. Energy from Biomass and Waste,
15, 745-765.
FLUENT INC., 1995, News Letter, 4(1).
FLUENT INC., 2001. FLUENT 6 User?s Guide, Volume 4, FLUENT INC., Lebanon,
NH.
FLUENT INC., 2006. MIXSIM 2.1 User?s Guide, FLUENT INC., Lebanon, NH.
Glazer, N. A., and Nikaido, H., 1995. Ethanol. In: Microbial Biotechnology. 359-391,
W. H. Freeman and Company, San Francisco.
Gould, J. M., 1984. Alkaline Peroxide Delignification of Agricultural Residues to
Enhance Enzymatic Saccharification, Biotechnol. Bioeng., 26, 46-52.
Grenville, R., 1995. Blending of Miscible Liquids in the Turbulent and Transitional
Regimes. Paper presented at Mixing XV, 15
th
Biennial North American Mixing
Conference, Banff, AL, Canada.
194
Grohmann, K., Torget, R., and Himmel, M., 1985. Optimization of Dilute Acid
Pretreatment of Biomass, Biotechnol. Bioeng. Symp., 15, 59-80.
Hacking, A. J., Taylor, I. W. F., and Hanas, C. M., 1984. Selection of yeasts able to
produce ethanol form glucose at 40
o
C, Appl. Microbiol. Biotechnol., 19, 361-
363.
Hettenhaus, J. R., 1998. Ethanol Fermentation Strains. In: Present and Future
Requirements for Biomass to Ethanol Commercialization, 1-25, United States
Department of Energy, National Renewable Energy Laboratory.
Hinze, J. O., 1989. Turbulence. McGraw-Hill Book Company, Inc., New York.
Himmel, M. E., Adney, W. S., Baker, J. O., Elander, R., McMillan, J. D., Nieves, R. A.,
Seehan, J. J., Thomas, S. R., Vinzant, T. B. and Zhang, M., 1997. Advanced
Bioethanol Production Technologies. A perspective. In: Fuels and Chemicals
from Biomass. B. D. Saha, J. Woodward (eds.) ACS Symp. Ser 666, American
Chemical Society, Washington, DC, Chapter 1, 1-45.
Hogsett, D. A., Ahn, H. J., Bernardez, T. D., South, C. R., and Lynd, L. R., 1992.
Direct microbial conversion: prospects, progress and obstacles, Appl. Biochem.
Biotechnol., 34/35, 527-541.
Houchin, T. L., and Hanley T. R., 2004. Measurement of rheology of distiller's grain
slurries using a helical impeller viscometer, Appl. Biochem. Biotechnol.,
113/116,723-32.
Hubbard, D. W., 1987. Scale-Up Strategies for Bioreactors. In: Biotechnology
Process Scale-Up and Mixing. (Ed.), Ho, C. S. and Oldshue, J. Y., 168-184. New
York: American Institute of Chemical Engineers.
Humphrey, A., 1998. Shake Flask to Fermentor: What have we learned? Biotechnol.
Prog., 14, 3-7.
Ingledew, W. M., 1993. Yeast for production of fuel ethanol. In: the yeast, 2
nd
ed.,
vol. 5. Yeast Technology. (Ed.), Rose, A. H., and Harrison, J. S., New York:
Academic Press.
Johnson, E. A., Sakojoh, M., Halliwell, G., Madia, A., and Demain, A. L., 1982.
Saccharification of complex cellulosic substrates by the cellulase system from
Clostridium thermocellum. Appl. Eviron. Microbiol., 43(5), 1123-1132.
Ju, L. K., and Chase, G. G., 1992. Improved scale-up strategies of bioreactors,
Bioprocess Engineering, 8, 49.
195
Kaar, W. E., and Holtzapple, M. T., 1998. Benefits from Tween during Enzymatic
Hydrolysis of Corn Stover, Biotechnol. Bioeng., 59, 419-427.
Karow, E. O., Bartholomew, W. H., and Sfat, M. R., 1953. Oxygen transfer and
agitation in submerged fermentations, Agricultural and Food Chemistry,
1(4):302.
Kadam, K. L., and McMillan, J. D., 2003. Availability of corn stover as a sustainable
feedstock for bioethanol production, Biores. Technol., 88, 17-25.
Klinke, H. B., Thomsen, A. B., and Ahring, B. K., 2001. Potential inhibitors form wet
oxidation of wheat straw and their effect on growth and ethanol production by
Thermoanaerobacter mathranii, Appl. Microbiol. Biotechnol., 57, 631-638.
Kobayashi, S., Shoda, S., Donnelly, M. J., and Church, S. P., 1999. Enzymatic
synthesis of cellulose, Methods on Biotechnology, 10. In: Bucke, C. (Ed.),
Carbohydrate Biotechnology, Humana Press Inc., NJ, USA.
Kuipers. J. A. M., and van Swaaij, W. P. M., 1997. Application of computational fluid
dynamics to chemical reaction engineering. Rev. Chem. Eng., 13, 1-118.
Labuza, T. P., Barrera Santos, D., Roop, R. N., 1970. Engineering factors in single-cell
protein production. I. Fluid properties and concentration of yeast by
evaporation, Biotechnology and Bioengineering, 12, 123-134.
Launder, B. E., and Spalding, D. B., 1972. Lectures in Mathematical Models of
Turbulence. Academic Press, London.
Lawford, H. G., Rousseau, J. D., Mohagheghi, A., and McMillan, J. D., 1999.
Fermentation Performance Characteristics of a Prehydrolyzate-Adapted Xylose-
Fermenting Recombinant Zymomonas in Batch and Continuous Fermentations,
Appl. Biochem. Biotechnol., 77/79, 191-204.
Li, Z. J., Shukla, V., Wenger, K., Fordyce, A., Pedersen, A. G. and Marten, M., 2002.
Estimation of hyphal tensile strength in production-scale Aspergillus oryzae
fungal fermentations, Biotechnol. Bioeng., 77, 601-613.
L?bbert, A., and J
?
rgensen, B. S., 2001. Bioreactor performance: a more scientific
approach for practice, J. Biotechnol., 85(2), 187-212.
Lynd, L., Weimer, P. J., van Zyl, W. H., and Pretorius, I. S., 2002. Microbial Cellulose
Utilization: Fundamentals and Biotechnology, Microbiol. Mol. Biol. Rev.,
6(3):506-577.
196
Mancini, M., and Moresi, M., 2000. Rheological behaviour of baker?s yeast
suspensions, Journal of Food Engineering, 44,225-231.
Mansfield, S. D., Mooney, C., Saddler, J. N., 1999. Substrate and enzyme
characteristics that limit cellulose hydrolysis, Biotechnol. Prog., 15:804-816.
Mavituna, F., 1996. Strategies for Bioreactor scale-up in Computer and Information
Science Application in Bioprocess Engineering (Moreira, A. R., and Wallace, K.
K.), Kluwer Academic Publisher, Dordrecht, Netherlands.
Maxon, W. D., and Johnson, M. J., 1953. Aeration Studies on Propagation of Baker?s
Yeast, Ind. Eng. Chem., 45(11), 2554-2560.
McMillan, J. D., 1994. Pretreating Lignocellulosic Biomass: A Review, in Enzymatic
Conversion of Biomass for fuel Production, Himmel, M. E., Baker, J. O., and
Overend, R. P., eds. ACS Symp. Ser. 566. Washington, DC: American
Chemical Society, Chapter 15, 685-696.
Metz, B., Kossen, N. W. F., and Van Suijdam, J. C., 1979. The Rheology of Mould
Suspensions, Adv. Biochem. Eng., 11, 103-155.
Metzner, A. B., and Otto, R. E., 1957. Agitation of Non-Newtonian Fluids. AIChE J.,
3(1), 3-10.
Micale, G., Scuzzarella, A., Lettieri, P., Grisafi, F., and Brueato. A., 2002. CFD
simulations of solids suspension in slurred vessels with dense particle effects,
Proc. 8
th
Int. Conference: Multiphase Flow in Industrial Plants, 468-484 (ANIMP:
Milan. Italy).
Mohagheghi, A., Tucker, M., Grohman, K, and Wyman, C. E., 1992. High Solid
Simultaneous Saccharification and Fermentation of Pretreated Wheat Straw to
Ethanol, Appl. Biochem. Biotechnol., 33, 67-81.
Montante, G., Pinelli, D., and Magelli, F., 2002. Diagnosis of solids distribution in
vessels stirred with multiple PBT?s and comparison of two modeling approaches,
Can. J. Chem., Eng., 80, 665-673.
Montross, M. D., and Crofcheck, C. L., 2004. Effect of stover fraction and storage
method on glucose production during enzymatic hydrolysis, Bioresource
Technology, 92, 269?274.
Murray, M. Y., Chisti, Y., and Vlach, D., 1993. Fermentation of Cellulosic Material to
Mycoprotein Foods, Biotech. Adv., 11, 469-479.
197
Nguyen, Q. A., Tucker, M. P., Boynton, B. L., Keller, F. A., and Schell, D. J., 1998.
Dilute Acid Pretreatment of Softwoods, Scientific Note, Appl. Biochem.
Biotechnol., 70/72, 77-87.
?hgren, K., Rudolf, A., Galbe, M., and Larsson, S., 2006. Fuel ethanol production from
steam pretreated corn stover using SSF at high dry matter concentration,
Biomass Bioenergy, in press.
Oldshue, J. Y., 1983. Fluid Mixing Technology, McGraw-Hill, New York.
Ooshima, H., Burns, D. S., and Converse, A.O., 1990. Adsorption of Cellulase from
Trichoderma Reesei on Cellulose and Lignaceous Residue in Wood Pretreated
by Dilute Sulfuric Acid with Explosive Decompression, Biotechnol. Bioeng. 36,
446-452.
Padukone, N., 1996. Advanced Process options for bioethanol production. In:
Handbook on Bioethanol: Production and Utilization, Wyman, C. E. (Ed.)
Taylor and Francis. Washington DC, Chapter 14, 315-327.
Philipiddis G. P., Smith T. K., and Wyman, C. E., 1993. Study of the enzymatic
hydrolysis of cellulose for production of fuel ethanol by the simultaneous
saccharification and fermentation process, Biotechnol. Bioeng., 41, 846-853.
Philippidis, G. P., and Smith, T. K., 1995. Limiting factors in the simultaneous
saccharification and fermentation process for conversion of cellulosic biomass to
fuel ethanol, Appl. Biochem. Biotechnol., 51/52, 117-124.
Philippidis, G. P., 1996. Cellulose bioconversion technology. In: Wyman, C. E.
(Ed.), Handbook on bioethanol: production and utilization. Taylor & Francis,
Washington, DC, 253-285.
Philippidis, G. P., and Hatzis, C., 1997. Biochemical Engineering Analysis of Critical
Process Factors in the Biomass-to-Ethanol Technology, Biotechnol. Prog.,13(3),
222-231.
Pimenova, N. V., and Hanley T. R., 2003. Measurement of Rheological Properties of
Corn Stover Suspensions, Appl. Biochem.Biotechnol.,105/108,353-364.
Ranade, V. V., 2002. Computational Fluid Modeling for Chemical Reactor Engineering,
Academic Press: New York.
Ranatunga, T. D., Jervis, J., Helm, R. F., McMillan, J. D., and Wooley, R. J., 2000. The
Effect of Overliming on the Toxicity of Dilute Acid Pretreated Lignocellulosics:
the Role of Inorganics, Uronic Acids and Ether-Soluble Organics, Enzyme and
Microbial Technology, 27, 240-247.
198
Renewable Fuels Association (RFA), 2006. Ethanol Industry Outlook: From Niche to
Nation.
Renewable Fuels Association (RFA), 2003. Ethanol Industry Outlook: Building a
Secure Energy Future.
Reese, E. T., and Ryu, D. Y., 1980. Shear inactivation of cellulase of Trichoderma
reesei, Enzyme and Microbial Technology, 2(3), 239-240.
Rodi, W., 1980. Turbulence Models and their Applications in Hydraulics. IAHR
Monograph.
Roels, J. A., Van Der Berg, J., and Voncken, R. M., 1974. The Rheology of Mycelial
Broths, Biotechnol. Bioeng., 16, 181-208.
Rushton, J. H., Costich, E. W., and Everett, H. J., 1950. Power Characteristics of
Mixing Impellers. Chem. Eng. Prog., 9, Part I: 395-450, Part II: 467-476.
Saxena, A., Garg, S. K., and Verma, J., 1992. Simultaneous saccharification and
fermentation of waste newspaper to ethanol, Bioresource Technol., 39, 13-15.
Schell, D., Nguyen, Q., Tucker, M., and Boynton, B., 1998. Pretreatment of Softwood
by Acid-Catalyzed Steam Explosion Followed by Alkali Extraction, Appl.
Biochem. Biotechnol., 70/72, 659-663.
Schell, D. J., Walter, P. J., and Johnson, D. K., 1992. Appl. Biochem. Biotechnol.,
23/35, 659-663.
Scott, C. D., Brian H., Scott, D. T. C., Woodward, J., Dees C., and Dena, S., 1994. An
Advanced Bioprocessing Concept for the Conversion of Waste Paper to Ethanol,
Appl. Biochem. Biotechnol., 45/36, 641-653.
Sha, Z., Palosaari, S., Oinas, P., and Ogawa, K., 1999. CFD simulation of solid
suspension in a stirred tank, Proceedings 3
rd
Int. Symp. Mixing in Industrial
Processes, Osaka, 29-36 (Society of Chemical Engineers: Tokyo, Japan).
Shiang, M., 1985. Production, Action and Denaturation of the Cellulases of
Trichoderma Reesei Rut-C30 on Different Cellulose. Master Thesis, Colorado
State University.
Speers, R. A., Durance, T. D., Tung, M. A., and Tou, J., 1993. Colloidal properties of
flocculent and nonflocculent brewing yeast suspensions, Biotechnology Progress,
9, 267-272.
199
Spindler, D., Wyman, C. E., Mohagheghi, A., and Grohman, K., 1988. Thermotolerant
Yeast for Simultaneous Saccharification and Fermentation of Cellulose to
Ethanol. Appl. Biochem. Biotechnol., 17, 279-293.
Steffe, J. F., 1996. Rheological Methods in Food Process Engineering. Freeman Press.
Sternberg, D., 1976. Production of cellulase by Trichoderma, Biotechnol. Bioeng.
Symp., 35-53.
Sundaresan, S., 2000. Modeling the hydrodynamics of multiphase flow reactors:
current status and challenges, AlChE J., 46, 1102-1105.
Sun, Y., and Cheng, J., 2002. Hydrolysis of lignocellulosic materials for ethanol
production: a review, Bioresource Technol., 83 (1), 1?11.
Szczodrak, J., and Targonski, Z., 1989. Simultaneous saccharification and fermentation
of cellulose: Effect of ethanol and cellulases on particular stages, Acta.
Biotechnol., 6, 555-564.
Tamerler, C. and Keshavarz, T., 1999. Optimization of agitation for production of
swainsonine from Metarhizium anisopliae in stirred tank and airlift reactors,
Biotechnology Letters, 21, 501-504.
Takagi, M., Abe, S., Suzuki, S., Emert, G. H., and Yata, N., 1978. A method for
production of alcohol directly from cellulose using cellulase and yeast, In:
Ghose, T. K. (Ed.), Proceedings of bioconversion of cellulosic substances into
energy, chemical and microbial protein, I. I. T, New Delhi, India, 551-571.
Tengborg, C., Stenberg, K., Gable, M., Zacchi, G., Larsson, S., Palmqvist, E. and Hahn-
Hagerdal, B., 1998. Comparison of SO
2
and H
2
SO
4
Impregnation of Softwood
Prior to Steam Pretreatment on Ethanol Production, Appl. Biochem. Biotechnol.,
70/72, 3-15.
Tengerdy, R. P., and Nagy, J. G., 1988. Increasing the feed value of forestry waste by
ammonia freeze explosion treatment, Biol. Wastes, 25, 149-153.
Teymuri, F., Laureano-Perez, L., Alizadeh, H., and Dale, B. E., 2005. Optimization of
the ammonia fiber explosion (AFEX) treatment parameters for enzymatic
hydrolysis of corn stover, Biores. Techol., 96, 2014-2018.
Tyson, K. S., Riley, C. J., and Humphreys, K. K., 1993. Fuel Cycle Evaluations of
Biomass Ethanol and Reformulated Gasoline. National Renewable Energy
Laboratory (NREL), Golden, CO, NREL/TP-463-4950.
200
Ulbrecht, J., and Carreau, P., 1985. Mixing of Viscous Non-Newtonian Liquids. Mixing
of Liquids by Mechanical Agitation, Ulbrecht, J., and Patterson, G. K., Eds.,
Gordon and Breach, New York, 93-137.
Um, B. H., 2002. Modeling of Acid Pretreatment and Enzymatic Hydrolysis of Corn
Stover, Master Thesis, Colorado State University.
Ursula, M., Ali R. E., and John N. S., 2002. Influence of Mixing Regime on Enzymatic
Saccharification of Steam-Exploded Softwood Chips, Appl. Biochem.
Biotechnol., 98/100, 463-472.
USA TODAY, Jun 11, 2006. Debate brews: Has oil production peaked ?
U. S. Department of Energy, 2001. Annual Energy Review, Energy Information
Administration. U. S. Washington, D. C., DOE/EIA, 0219.
U. S. Department of Energy, 2005. Alternative fuel.
V?ljam?e, P., Sild, V., Pettersson, G., and Johansson, G., 1998. The initial kinetics of
hydrolysis by cellobiohydrolases I and II is consistent with a cellulose surface-
erosion model, Eur. J. Biochem., 253(2):469-75.
Van den Akker, H. E. A., 1997. On status and merits of computational fluid dynamics,
Proceedings 4
th
Int. Conf. Bioreactor and Bioprocess Fluid Dynamics, 407-432,
(MEP/BHR: London), Edinburgh.
Varga, E., Klinke, H. B., R?czey, K., and Thomsen, A. B., 2004. High Solid
Simultaneous Saccharification and Fermentation of Wet Oxidized Corn Stover to
Ethanol. Biotechnol. Bioeng., 88, 567-574.
Wisconsin Biorefining Development Initiative, 2004.
Wright, J. D., 1988. Ethanol from biomass by enzymatic hydrolysis. Chem. Eng. Pro.,
84(8), 62-74.
Wyman, C. E., 1994. Ethanol from Lignocellulosic Biomass: Technology, Economics,
and Opportunities, Bioresource Technology, 50, 3-16.
Wyman, C. E., 1996. Handbook on Bioethanol: Production and Utilization. Taylor
and Francis, Washington, DC.
Wyman, C.E., 2003. Potential Synergies and Challenges in Refining Cellulosic
Biomass to Fuels, Chemicals, and Power, Biotechnol. Prog., 19, 254-262.
201
Wyman, D. K. B., Hinman, N. D., and Stevens, D. J. 1993. Ethanol and Methanol from
Cellulosic Biomass, Johannson, T. E., Kelly, H., Reddy, A. K. N., and Williams,
R. A. editors. Renewable Energy: Source for Fuels and Electricity,
Washington, DC: Island Press. 865-923.
Zhang, S., David, E., Wolfgang, D., and Wilson, B., 1999. Substrate Heterogeneity
Causes the Nonlinear Kinetics of Insoluble Cellulose Hydrolysis, Biotechnol.
Bioeng., 66, 35-41.
Zhang, Y. H. P., and Lynd, L., 2004. Toward an Aggregated Understanding of
Enzymatic Hydrolysis of Cellulose: Noncomplexed Cellulase Systems,
Biotechnol Bioeng. 88(7):797-824.
Zheng, Y. Z., Lin, H. M., and Tsao, G. T., 1998. Pretreatment for cellulose hydrolysis
by carbon dioxide explosion, Biotechnol. Prog., 14, 890-896.
202
APPENDIX A
MEDIA FOR FERMENTATION AND BIOREACTOR PROTOCOLS
203
A-1. AGAR MEDIA COMPOSITIONS
Solid Agar Medium
RM Agar
Yeast Extract 10g/L
KH
2
PO
4
2 g/L
Bacto-agar 15 g/L
Deionized Water 900 mL
20 % Glucose Solution 100 mL
Add yeast extract, KH
2
PO
4
, Bacto-agar, and DI-water to an autoclavable conical flask
and autoclave at 121
o
C for 20 minutes. After cooling, add the 20 % glucose solution
(200g glucose/1000 mL DI water). Pour the plates when the temperature of the medium
is about 45-50
o
C. Store the plates in the refrigerator.
NOTE: Filter-sterilize any sugar solutions >20% (w/v). All sugar solutions < 20 %
(w/v) should be autoclaved at 121
o
C for 20 minutes.
RMG Agar
RM agar + 2 % Glucose
RMX Agar
RM Agar + 2 % Xylose
RMGTc Agar
RM Agar + 2 % Glucose + 0.02 g/L Tetracycline
Liquid Culture Medium
RM Liquid (1L)
Yeast Extract 10 g/L
KH
2
PO
4
2 g/L
De-ionized Water 900 ml
20 % Glucose Solution 100 ml
204
Add yeast extract, KH
2
PO
4
, and DI-water to an autoclavable conical flask and autoclave
at 121
o
C for 20 minutes. After cooling, add the 20 % glucose solution. The liquid can
be kept at room temperature or in the refrigerator.
NOTE: Filter-sterilize any sugar solutions >20% (w/v). All sugar solutions < 20 %
(w/v) should be autoclaved at 121
o
C for 20 minutes.
RMG Liquid
RM Liquid + 2 % Glucose
RMX Liquid
RM Liquid + 2 % Xylose
RMGTc Liquid
RM Liquid + 2 % Glucose + 0.02 g/L Tetracycline
RM Liquid (200 ml; 10 % w/v solution to be added to 3 L reactor)
Yeast Extract 2.0 g/L
KH
2
PO
4
0.5 g/L
De-ionized Water 180 ml
20 % Glucose Solution 20 ml
Frozen/Liquid Cell Stocks 2.0 ml (1 % of 200 ml)
Make sure to autoclave liquid before adding glucose and bacteria. The bacterial
suspensions should be grown for about 24 hours or until the OD600 reached the desired
optical density. The optical densities needed for this work are 0.5 at 600nm in a 200 mL
solution.
RM Liquid for growing bacterial in 10 mL solution
Yeast Extract 0.1 g/L
KH
2
PO
4
0.02 g/L
De-ionized Water 9 ml
20 % Glucose Solution 1 ml
Bacteria 5~10 colonies
Make sure to autoclave liquid before adding glucose and bacteria.
205
A-2. REVIVAL AND GROWTH OF Zymomonas mobilis 39679 pZB 4L
The following procedure describes the revival and growth of Zymomonas mobilis
39679 pZB 4L from stabs or frozen stocks.
Step 1. Growing Zymomonas mobilis 39679 pZB 4L on solid agar medium.
1. Turn on incubation unit and make sure the temperature is set to 30
o
C.
2. On a new Petri dish containing RMGTc agar label (cell, date, name) the back
of the dish containing the agar.
3. Retrieve a frozen stock of cells from the freezer and warm by holding it in
your hand.
4. Turn on light and blower in the hood.
5. Wash inside the hood with a 70 % ethanol solution.
6. Place agar media in the hood.
7. Remove inoculating loop from package and place in the hood.
8. Wearing rubber gloves, spray with the 70 % ethanol solution.
9. Remove lid from the once frozen stock of bacterial and insert the inoculating
loop.
10. Streak the large section by rubbing the loop across the agar in a back and forth
motion.
11. Streak each smaller section next. For these sections start streaking in the
previous section first (see picture).
12. Streak each section 3 times so that cells are dilute enough to generate isolated
colonies.
206
13. Once plates have streaked, put top on and surround the dish with parafilm
laboratory film, ensuring that the top and bottom of the dish are tightly sealed
to exclude air.
14. Place the dish in the incubator and leave there for three days.
15. Rewash the hood with the 70 % ethanol solution and turn the light and blower
off.
16. Throw inoculating loops into red infections waste disposal can.
17. After 3days, either start next step in cultivation of bacteria or store in the
refrigerator until ready to begin next step.
18. Obtain three agar plates, one RMX, one RMGTc, and one RMG agar plate,
and label each plate with name, date and type of plate.
19. Using an inoculating loop, a different loop for each colony, and transfer up to
twenty colonies to the three plates.
20. Pick up each colony and streak each plate in the order of RMX, RMGTc, and
RMG. The streaking should be no more than a couple of streaks that the about
2 mm long.
21. Incubate each plate at 30
o
C for 3days.
22. After 3days, store the plates in a refrigerator no longer than two weeks.
Step 2. Growing Zymomonas mobilis 39679 pZB 4L in liquid medium.
23. Add K
2
HPO
4
, yeast extract, and DI water to an autoclavable glass tube
(amounts of each ingredient for different % w/v solutions can be found in
APPENDIX A-1).
207
24. Mix ingredients by inverting tube numerous times.
25. Place the tube in the autoclave for 20 minutes and wait 20 more minutes for
autoclave to cool slightly. When removing the tube from the autoclave, watch
for steam exiting the autoclave when it is opened.
26. Place the tube in the hood and allow it to cool completely.
27. Clean hood with a 70 % ethanol solution.
28. Under the hood, add glucose (20 %) to the tube using a sterilized pipet.
29. Pick one of the three plates containing the isolated 20 colonies.
30. Scrape off 5~10 colonies with an inoculating loop, under hood, and add to the
liquid in the tube.
31. Cap the tube and place in the incubator for 12 hours at 30
o
C.
32. After 12 hours, remove the tube from the incubator and obtain a sample from
the tube.
33. Measure the optical density of the sample taken from the tube.
34. Clean hood with the 70 % ethanol solution.
35. Gather 10 small corning 2 mL plastic sterilized tubes.
36. Under hood, add 0.5 mL of glycerol (60 %) to each tube.
37. Add 1 mL of the liquid culture media from the tube into each of the smaller
tubes.
38. Mix by inverting the vials several times.
39. Store the vials in a -70
o
C freezer.
208
Figure A-1. Preparation and representative streak plate.
209
A-3. SET UP OF BioFlo 3000 (New Brunswick Scientific)
Fermentation Procedure
1. Replace the glass vessel onto the BioFlo stand. Set the vessel on so that the New
Brunswick logo faces front.
2. Attach glass vessel to the heat exchange vessel using thumbscrews. Be sure that
the steel ring is centered around the vessel, or it will leak. These screws should
be secured as hand tight as possible, do not use any tools.
3. Place baffle assembly into the glass vessel such that the 2nd baffle (counting
around the ring counter clockwise) is centered between the additions port directly
below the New Brunswick logo and the port to its left.
4. Pour in media; make sure that both impellers are submerged.
5. Place the head plate onto the vessel and lock it to the clamping ring by tightening
the thumbscrews.
6. Add 9 ml of Antifoam-A through the inoculation port.
7. Insert the temperature probe (RTD) with a few drops of glycerol into the
thermowell and plug into the BioFlo 3000.
8. Place steel blank into condenser port with a few drops of glycerol and gently
tighten.
9. Place 1/4 I.D. silicone tubing on the top of the steel blank on condenser and
connect the air filter (Acro 50) such that there is tubing attached to both sides and
the tube from the condenser is attached to the 'inlet' side of the filter (imprinted on
filter). Bend last tube in half and secure with cable tie such that nothing can
escape through the tubing. Wrap cotton and aluminum foil around the exposed
tubing end.
10. Obtain two long (>60cm) silicone tubes.
11. Attach one acid/base silicone tubing (I.D. 1/32) to one of the addition ports.
Bend tubing in half close to port and secure with a cable tie. Wrap cotton and
aluminum foil around the exposed tubing end.
12. Attach the other acid/base silicone tubing (I.D. 1/32) to the other addition port.
Bend tubing in half close to port and secure with a cable tie. Wrap cotton and
aluminum foil around the exposed tubing end.
210
13. Place 1/4 I.D. silicone tubing on the top of the sparger tube and connect the air
filter (Acro 37) such that there is tubing attached to both sides. Wrap cotton and
aluminum foil around the exposed tubing end.
14. Attach silicone tubing to the harvest port, bend tube in half near port opening and
secure with cable tie. Wrap cotton and aluminum foil around the exposed tubing
end.
15. Loosen the inoculation port to allow ventilation.
16. Loosen the sampling tube, and remove the rubber bulb, make sure that there is an
ample amount of glass wool in tube where the rubber bulb connects to the
sampling assembly.
17. Make sure that the sampling valve is closed.
18. Connect the water out line (top) to the vessel heat exchanger (the vessel base).
19. Connect the water in lines (bottom) to the vessel heat exchanger.
20. TURN ON WATER (under the counter)
Note: The pressure gauge should read 15 psig.
21. TURN ON AIR (Use SOP on wall above tanks)
Note: The pressure gauge should not exceed 10 psig, 5 psig is safe.
22. TURN ON BioFlo 3000.
Calibration of pH Probe
23. Check the probe for any trapped air bubbles, tap gently at a 45 degree angle to
remove bubbles.
24. Check electrolyte levels within the probe. They should be around 1 cm below
the each of the filling ports.
25. Remove the rubber plugs.
26. Attach one end of the pH cable to the pH probe and the other end of the pH cable
to BioFlo console.
27. Go to screen.
211
28. Immerse pH electrode into a pH 7 buffer solutions.
Note: Allow a few minutes for the electrode to equilibrate.
29. Set function column (use arrow keys to move selection on display) to ZERO by
pressing alter and press enter.
30. Enter seven (7.0) under the zero columns and press enter.
31. Rinse electrode thoroughly with de-ionized water.
32. Immerse pH electrode into a pH 4 buffer solutions.
Note: Allow a few minutes for the electrode to equilibrate.
33. Set function column to SPAN using alter key and press enter.
34. Enter four (4.0) under the span column and press enter.
35. Rinse electrode with de-ionized water.
36. Repeat calibration.
37. Apply a small amount of glycerol to the probe prior to inserting into the vessel
38. Disconnect the cable, and replace shorting caps and rubber plugs prior to
sterilization. Rubber bands, located on the pH probe, should be placed over the
rubber plugs.
Installation of Dissolved Oxygen Probe
39. Remove protective cap (green) from the electrode end.
40. Attach adapter to head plate and tighten with a wrench.
41. Carefully insert the probe into the adapter.
42. The shorting plug (cap on top of probe) should be installed prior to sterilization.
Autoclaving Procedure
43. Turn off the power.
44. Turn off the water.
212
45. Turn off the air.
46. Disconnect water out lines to heat exchanger.
47. Disconnect water in lines to heat exchanger.
48. Remove RTD from the thermowell.
49. Double check all tubes and ports to ensure vessel is completely sealed up, except
for inoculation port.
50. Autoclave entire assembly at 121?C at 15 psig for 25 minutes.
Preparation for Operation
51. Place the BioFlo vessel onto the console.
52. Connect the water out line to the vessel heat exchanger (the vessel base).
53. Connect the water in lines to the heat exchanger.
54. Connect the water out line to the condenser. (Top)
55. Connect the water in line to the condenser. (Bottom)
56. Add glycerol to thermowell and insert RTD.
57. TURN ON NITROGEN
Note: Approximately 5 psig, do not exceed 10 psig.
58. TURN ON WATER (under the counter) Make sure that the water pressure is
between 15 and 20 psig.
59. Connect the air line (tube) from sparger to the sparger 4 gas port on the BioFlo
base (near the top).
60. Turn on power switch.
61. Select Fermentation mode (#2)
62. Go to screen and set the temperature control mode to PRIME for one
minute.
213
63. After a minute set the desired working temperature (30?C) and the control mode
to PID.
64. Remove the rubber plugs and shorting cap from the pH probe and connect the pH
cable.
65. Remove shorting cap from DO probe and connect DO cable.
66. Place motor onto the top of the head plate and plug into the console.
Attaching the Condenser
67. Go to the screen and set the mode to MANUAL, DO NOT PRESS
ENTER.
68. Remove the foil and place exhaust tubing from condenser and filter such that is
collects into a beaker that is placed beside the BioFlo console.
69. Press enter.
Setting-up the pH Control
70. Place the agitation loop into PID control and set the set point to 120 rpm.
71. Attach the end of the tube from the addition port to the top of the glass tube on
the ammonium hydroxide flask.
72. Attach the ammonium hydroxide through the peristaltic pump. Thread the tube
in the following manner: from the addition port, thread the tube through the
bottom of the pump and around to the top, and onto the ammonium hydroxide
flask glass tube on top. The pump moves in a clockwise direction, therefore the
solution will move in that direction. Verify that the ammonium hydroxide will
be pumped into the vessel before proceeding to the next step.
73. Set the pH loop to PID control and set the set point to 5.0.
74. Set the feed pump 1 control loop (use alter key while on pH loop to get to feed
pump 1 loop) to BASE, and the set point to 100.
214
Calibration of the Dissolved Oxygen Probe
Note: Probe cannot be calibrated until the desired working temperature has been
reached.
75. Go to screen, arrow to Function column for DO.
76. Unplug DO cable from the DO probe.
77. Set Function column to ZERO and press enter.
78. Arrow to zero column and enter zero (0.0) and press enter.
79. Reattach cable to DO probe.
80. Go to screen and set Agitation to 1000 rpm.
81. Go to screen and set the mode to MANUAL.
82. Allow ten to thirty minutes for the vessel to equilibrate.
83. Go to screen, arrow to Function column for DO.
84. Set Function column to SPAN and press enter.
85. Arrow to Span column and enter 100.0 and press enter.
215
APPENDIX B
RHEOLOGY AND RHEOLOGICAL PARAMETER DETERMINATION
216
0.01
0.1
1
10
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
o
s
i
t
y (
P
a*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
0.5
1
1.5
2
2.5
3
3.5
0 50 100 150
Shear Rate (sec
-1
)
S
h
e
a
r S
t
res
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure B-1. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 10 % (w/v).
(B-1a)
(B-1b)
217
0.01
0.1
1
10
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
0.5
1
1.5
2
2.5
3
3.5
4
0 50 100 150
Shear Rate (sec
-1
)
S
h
ear S
t
r
e
s
s
(
P
a)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure B-2. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 15 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 10 % (w/v).
(B-2a)
(B-2b)
218
0.01
0.1
1
10
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a
*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
1
2
3
4
5
6
7
0 50 100 150
Shear Rate (sec
-1
)
S
h
e
a
r S
t
re
s
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure B-3. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 30
o
C, 120 rpm.
2. Substrates were added to the reactions 10 % (w/v).
(B-3a)
(B-3b)
219
0.01
0.1
1
10
100
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
sco
s
i
t
y
(
P
a*s)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
2
4
6
8
10
12
14
0 50 100 150
Shear Rate (sec
-1
)
S
h
ea
r S
t
res
s
(
P
a
)
t= 1hr t=2 hr
t=3 hr t=4 hr
Figure B-4. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 15 % (w/v).
(B-4a)
(B-4b)
220
0.01
0.1
1
10
100
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a
*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
2
4
6
8
10
12
14
16
18
0 50 100 150
Shear Rate (sec
-1
)
She
a
r
St
r
e
s
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure B-5. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 15 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 15 % (w/v).
(B-5a)
(B-5b)
221
0.1
1
10
100
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
5
10
15
20
25
0 50 100 150
Shear Rate (sec
-1
)
She
ar
St
r
e
s
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure B-6. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 30
o
C, 120 rpm.
2. Substrates were added to the reactions 15 % (w/v).
(B-6a)
(B-6b)
222
0.1
1
10
100
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
o
s
i
t
y (
P
a*
s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
5
10
15
20
0 50 100 150
Shear Rate (sec
-1
)
S
h
ea
r S
t
re
s
s
(
P
a
)
t=1 hr t=2 hr
t=3 hr t=4 hr
Figure B-7. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 20 % (w/v).
(B-7a)
(B-7b)
223
0.1
1
10
100
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a
*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
5
10
15
20
25
30
05010150
Shear Rate (sec
-1
)
She
a
r
St
r
e
s
s
(
P
a
)
t= 1hr t=2 hr
t=3 hr t=4 hr
Figure B-8. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 15 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 20 % (w/v).
(B-8a)
(B-8b)
224
0.1
1
10
100
0.1 1 10 100 1000
Shear Rate (sec
-1
)
V
i
s
c
os
i
t
y (
P
a*s
)
t=1 hr t=2 hr
t=3 hr t=4 hr
0
5
10
15
20
25
30
0 50 100 150
Shear Rate (sec
-1
)
She
a
r
St
r
e
s
s
(
P
a
)
t= 1hr t=2 hr
t=3 hr t=4 hr
Figure B-9. Viscosity and shear stress curves as a function of shear rate for different time during initial 4-hours
enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 30
o
C, 120 rpm.
2. Substrates were added to the reactions 20 % (w/v).
(B-9a)
(B-9b)
225
Table B-1. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 10 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 0.142 0.167 0.560 0.862 0.636 0.026 0.904 0.532 0.093 0.893 0.673 0.266 0.811
t= 2 hr 0.253 0.075 0.681 0.894 0.345 0.020 0.936 0.274 0.090 0.908 0.382 0.315 0.812
t= 3 hr 0.164 0.157 0.518 0.924 0.222 0.018 0.970 0.164 0.091 0.902 0.235 0.390 0.856
t= 4 hr 0.134 0.036 0.802 0.943 0.166 0.017 0.975 0.116 0.091 0.937 0.177 0.430 0.858
226
Table B-2. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 15 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 10 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 0.937 0.020 1.045 0.908 0.924 0.024 0.904 0.786 0.082 0.854 0.940 0.205 0.732
t= 2 hr 0.620 0.034 0.907 0.914 0.645 0.023 0.922 0.537 0.085 0.882 0.663 0.248 0.777
t= 3 hr 0.405 0.040 0.821 0.919 0.447 0.019 0.943 0.364 0.081 0.897 0.459 0.278 0.793
t= 4 hr 0.338 0.025 0.911 0.937 0.354 0.017 0.950 0.281 0.080 0.898 0.359 0.304 0.797
227
Table B-3. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 30
o
C, 120 rpm.
2. Substrates were added to the reactions 10 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 1.391 0.009 1.426 0.993 1.224 0.052 0.970 0.895 0.139 0.916 1.079 0.314 0.824
t= 2 hr 0.535 0.076 0.855 0.978 0.602 0.042 0.980 0.418 0.138 0.970 0.561 0.389 0.907
t= 3 hr 0.388 0.039 0.948 0.948 0.404 0.031 0.968 0.269 0.122 0.943 0.383 0.406 0.872
t= 4 hr 0.296 0.028 0.985 0.985 0.299 0.026 0.978 0.187 0.117 0.953 0.266 0.453 0.896
228
Table B-4. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 15 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 4.308 0.868 0.454 0.991 5.624 0.069 0.957 4.774 0.124 0.988 4.841 0.169 0.977
t= 2 hr 2.352 0.666 0.503 0.991 3.339 0.070 0.968 2.630 0.147 0.997 2.752 0.240 0.987
t= 3 hr 1.343 0.264 0.533 0.991 1.743 0.032 0.969 1.407 0.095 0.992 1.460 0.219 0.975
t= 4 hr 1.429 0.149 0.614 0.979 1.664 0.027 0.967 1.378 0.082 0.980 1.422 0.195 0.954
229
Table B-5. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 15 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 15 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 4.683 0.952 0.463 0.997 6.138 0.078 0.962 5.167 0.135 0.994 5.248 0.175 0.982
t= 2 hr 2.749 0.686 0.507 0.999 3.779 0.073 0.969 3.013 0.146 0.997 3.133 0.228 0.986
t= 3 hr 2.352 0.666 0.503 0.999 3.339 0.070 0.968 2.630 0.146 0.997 2.752 0.240 0.987
t= 4 hr 2.111 0.651 0.499 0.999 3.066 0.067 0.968 2.399 0.146 0.997 2.520 0.247 0.988
230
Table B-6. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 30
o
C, 120 rpm.
2. Substrates were added to the reactions 15 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 3.952 3.604 0.324 0.992 8.717 0.143 0.933 7.134 0.199 0.980 7.320 0.210 0.989
t= 2 hr 3.184 2.157 0.376 0.997 6.792 0.115 0.946 5.519 0.180 0.988 5.671 0.214 0.991
t= 3 hr 3.341 2.194 0.366 0.996 6.329 0.111 0.944 5.126 0.178 0.987 5.279 0.219 0.992
t= 4 hr 2.714 1.855 0.376 0.997 5.238 0.101 0.947 4.193 0.173 0.988 4.347 0.230 0.992
231
Table B-7. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 20 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 9.576 0.849 0.488 0.995 10.986 0.076 0.960 9.835 0.109 0.993 9.861 0.111 0.966
t= 2 hr 5.614 4.393 0.206 0.996 11.063 0.074 0.916 9.918 0.108 0.975 9.851 0.115 0.993
t= 3 hr 5.727 2.712 0.283 0.997 9.335 0.080 0.934 8.198 0.121 0.983 8.185 0.135 0.992
t= 4 hr 3.217 3.721 0.236 0.997 7.883 0.081 0.920 6.795 0.131 0.977 6.800 0.157 0.996
232
Table B-8. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 15 FPU/g of glucan, pH 4.8~5.0, 50
o
C, 120 rpm.
2. Substrates were added to the reactions 20 % (w/v).
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 9.685 2.096 0.459 0.999 12.877 0.169 0.961 10.788 0.201 0.995 10.966 0.179 0.984
t= 2 hr 7.175 2.155 0.404 0.999 10.324 0.129 0.949 8.686 0.175 0.993 8.793 0.176 0.989
t= 3 hr 4.869 3.501 0.307 0.999 9.524 0.122 0.932 7.999 0.172 0.985 8.077 0.181 0.996
t= 4 hr 4.540 3.590 0.301 0.999 9.278 0.123 0.931 7.783 0.172 0.984 7.861 0.183 0.996
233
Table B-9. Determination of rheological parameter as function of time during initial 4-hr enzymatic hydrolysis.
Note;
1. Hydrolysis condition: 30 FPU/g of glucan, pH 4.8~5.0, 30
o
C, 120 rpm.
2. Substrates were added to the reactions 20 % in one time.
Nomenclatures;
?: shear stress, Pa
?
y
: yield shear stress, Pa
n: flow behavior index
K: consistency index constant, Pas
n
R
2
: squared multiple correlation coefficient
Herschel-Bulkley Model Bingham Model Casson Model Power Law
n
K
y ?
??
?
+=
?
??
?
+= K
y
5.05.05.0
)()(
?
??
?
+= n
y
n
K
?
?
?
=
Reaction
Time
?
y
(pa) K n R
2
?
y
(pa) n R
2
?
y
(pa) n R
2
K n R
2
t= 1 hr 12.385 0.8041 0.5729 0.996 13.777 0.112 0.976 12.193 0.137 0.996 12.275 0.1227 0.962
t= 2 hr 9.260 1.8764 0.4096 0.997 12.076 0.115 0.955 10.497 0.149 0.993 10.544 0.1431 0.985
t= 3 hr 7.755 2.0681 0.3910 0.997 10.768 0.116 0.953 9.244 0.156 0.992 9.309 0.1567 0.988
t= 4 hr 8.058 1.4869 0.4520 0.999 10.344 0.115 0.962 8.842 0.157 0.997 8.929 0.1591 0.984
234
APPENDIX C
SIMULATION METHODS, PROCEDURES, AND APPRATUS
235
C-1. START MIXSIM AND FLUENT
The simulations were performed using software package:
1. MixSim V 2.1.10
2. FLUENT V 6.2.20
1. Starting Mixsim
When IBM system is used, MixSim can be started by typing Mixsim from the
command line of an xterm window (Hummingbird). The Mixsim console window will
appear on the computer screens. The TUI, GUI will show up when new simulation new
model starts. GUI showing a tank with some of the default dimensions will open.
2. Starting the Session
Model
From the model, analysis type and tank type were selected - Geometry (Multiple
Reference Frame), or geometry (sliding mesh). In most simulations 3D cylindrical with
the velocity data model was chosen.
Continuum and Fluid
Viscous model and fluid properties category form the continuum was selected to
enter the appropriate flow regime and physical properties of the fluid, including the fluid
density and viscosity.
Cylindrical Tank
The Tank Geometry definition includes: Bottom Shape with choices of ASME
Dish, spherical, conical, curved, Tank Diameter, Tank Height, and Liquid Height. In all
236
simulation an Ellipsoidal was chosen to be the tank bottom shape and a top-surface is a
wall choice was deactivated to impose a slip boundary condition on the upper surface of
the liquid. After all dimensions were entered, by clicking Apply button, the tank outline
in the graphics window was drawn accordingly.
3. Add Object
Drive Shaft
In defining a top shaft, a distance off bottom, shaft diameter, rotational direction
(clockwise or counter-clockwise), and rotational speed need to be determined. The
distance off bottom of the shaft set the length of the shaft. The shaft had to go as far as
the lowest impeller in the tank. If this condition was not chosen, an error message will
appear.
Impellers (Rushton Turbine)
An impeller is added to the tank by clicking the add button. The data from the
library can be edited in the edit menu that includes impeller characteristics. General
impeller characteristics include diameter, axial location, the name of the blade (which can
be edited), number of blades of the impeller. In the parametric characteristics, either the
library data for the impeller detail dimensions or edited data can be used. When using
the impeller, the velocity data is provided in the MixSim Library. The location where
the velocity data is applicable could be below, above, or at the outer radius of the
impeller. Data for impellers could also be imported from an external file in the
Geometric Impeller Characteristics category. The correlations category includes flow
number and power number for the impeller. The most updated tank outline equipped
237
with impellers and draft tube was shown in the graphic window after clicking on the
Apply button.
Baffles
In all simulations, baffles were used. Four baffles were used in the Bioreactor
tank simulations. When determining the size of the baffle, the length of the baffles was
designed so that the baffles would not extend below the tank bottom and the radial width
would be 10 percent of the tank diameter.
4. Generate Grid and Grid Check
To create the geometry and generate the grid, MixSim uses GAMBIT in the
background. When the geometry is created, MixSim saves the GAMBIT files. You
can use these files to generate the mesh on your own or add custom objects using
GAMBIT.
The grid is generated in GAMBIT and then exported as a .msh file. The mesh file is
read into MixSim automatically and the grid is checked during the process. As the grid
is checked, MixSim reports on the grid properties and the grid quality by displaying the
cell distribution based on skewness level in the console.
5. Reporting the Model Information
In the Report pull-down menu, the Model Info category was selected to examine the
model information in report format. The complete report can be sent either to console
or to file.
238
6. Writing the MixSim File
The MixSim file had to be written to save the completely specified problem. This file
will contain all of the information that has been entered in the session and can be read
into MixSim at a later time to execute the simulation or to make modification to the
problem definition.
In the File pull-down menu, Write MixSim category was selected to write the MixSim
file. A name for the file was chosen and the file was written by clicking on the OK
button.
7. Performing the Calculation
The completely specified problem was ready to solve. In the Solve pull-down menu,
Calculate category was chosen to start the calculation. Once the number of iterations
was entered, the iteration began starting with grid generation by preBFC. The iteration
was performed by FLUENT 6.2.10.
When the session file in the MixSim session was not completed, entering zero iteration as
the number of iterations did enabling the writing of the case file of the problem. This
step allowed the generation of the grid so that the case file could be written. Once the
case file has been written, the calculation began by selecting the Iteration category under
the Solve pull-down menu and entering a number of iterations.
8. Examination the Results (MixSim 2.1.10 or FLUENT 6.2.20)
The solution of the iterations was examined using some of the graphics features provided
by FLUENT or MixSim under the Display pull-down menu. These features include
239
plotting vectors and contours of the flow field. Slices of the geometry in the x-direction
(radial-direction) and z-direction (angular direction) were created to better show the flow
pattern of the area of concern. In most cases velocity magnitude vectors, radial velocity
vectors, axial velocity vectors and tangential velocity vectors were examined. Energy
dissipation rate throughout the tank was presented in the contour plot.
9. Saving the Graphics Feature from FLUENT
The graphics presentations were improved using the View command in the Display pull-
down menu. The graphics features presented in a graphics window were rotated for
better viewing. These features were then saved as hardcopy files in postscript type of
file to enable them to be printed using a personal computer. Adobe Acrobat 7.0
Professional program was used to view and print this graphics feature.
10. Writing FLUENT Files
When the session file was not completed in MixSim, a new name had to be entered as a
data file to enable the file to be written. To write a data file the write data category was
selected in the file pull-down menu and name of the file was entered.
240
Figure C-1. The graphical user interface (GUI) components (MixSim 2.1.10).
241
Figure C-2. Scaled residuals.
Figure C-3. Histogram of tank cell equiangular skew.
242
Table C-1. Geometry of 3 L mixing tank.
BioFlo 3000 (3L Mixing Tank, NBS)
Cylindrical Tank
Diameter (m) 0.138
Tank Height (m) 0.265
Liquid Level (m) 0.177
Top Style Flat
Bottom Style Ellipsoidal
Flat Baffles
Bottom Elevation (m) 0.042
Top Elevation (m) 0.185
Width (m) 0.008
Top Shaft
Shaft Diameter (m) 0.0025
Speed (rpm) 120
Shaft Tip Elevation (m) 0.0503
Impeller (Rushton I)
Diameter (m) 0.046
Axial Location (m) 0.114
Number of Blade 6
Impeller (Rushton II)
Diameter (m) 0.046
Axial Location (m) 0.053
Number of Blade 6
Ring Sparger
Ring Diameter (m) 0.050
Tube Diameter (m) 0.003
Axial Location (m) 0.023
243
Table C-2. Blend time and flow rate (RPM=120).
Blend Time for a Single Impeller
cb
Z
T
T
D
aN
t
?
?
?
?
?
?
?
?
?
?
?
?
=
605.4
99
N(rps) 2 a 1.06
D(m) 0.046 b 2.17
T(m) 0.138 c 0.5
Z(m) 0.177 T
99
(s) 26.7446
N
b
6
ii
cb
ii
Z
T
T
D
Na
t
?
?
?
?
?
?
?
?
?
?
?
?
=
?
605.4
99
Blend Time for All Impellers In the Vessel
T
99, eff
(s) 13.3723
Radial Disk Impellers (for flow rate calculations)
c
b
b
b
Q
T
D
D
WN
aN
?
?
?
?
?
?
?
?
?
?
?
?
=
6
a 6 c 0.3
b 0.7
Flow Rate
3
NDNQ
Q
=
N (rps) 2 N
q
0.8628
D (m) 0.046 Q(m
3
/s) 0.0002
244
Table C-3. Power draw and correlation for water simulation (?=0.0010, RPM=120).
Impeller Type (for power draw calculations)
b
bb
p
N
D
W
aN ?
?
?
?
?
?
?
?
?
?
?
?
=
62.0
W
b
(m) 0.0092 a 5
N
b
6 b 0.8
D(m) 0.046
Power Draw
53
DNNP
p
?=
? (kg/m
3
) 998.2 N
p
5
N (rps) 2 P(W) 0.0082
D(m) 0.046 ?(Kg/m?s) 0.0010
Report Torque (Top Shaft)
Upper Impeller
Shaft Torque (n-m) 0.00064612
Shaft Power (W) 0.00811943
Impeller Torque (n-m) 0.00033790
Impeller Power (W) 0.00424617
Lower Impeller
Shaft Torque (n-m) 0.00064612
Shaft Power (W) 0.00811943
Impeller Torque (n-m) 0.00030822
Impeller Power (W) 0.00387326
Correlations
Single or Multiple Impellers
Number of Impellers 2
Blend Time (s) 13.3723
Single Impeller
Impeller Type Radial
Reynolds Number 4203.33
Froude Number 0.0187
Power Draw (w) 0.0082
Flow Rate (m
3
/s) 0.0002
245
Table C-4. Power draw and correlation for 10 % Solka Floc fermentation broth
(?=0.0192, RPM=120).
Impeller Type (for power draw calculations)
b
bb
p
N
D
W
aN ?
?
?
?
?
?
?
?
?
?
?
?
=
62.0
W
b
(m) 0.0092 a 5
N
b
6 b 0.8
D(m) 0.046
Power Draw
53
DNNP
p
?=
? (kg/m
3
) 1130 N
p
5
N (rps) 2 P(W) 0.0093
D(m) 0.046 ?(Kg/m?s) 0.0192
Report Torque (Top Shaft)
Upper Impeller
Shaft Torque (n-m) 0.00075451
Shaft Power (W) 0.00948143
Impeller Torque (n-m) 0.00038094
Impeller Power (W) 0.00478698
Lower Impeller
Shaft Torque (n-m) 0.00075451
Shaft Power (W) 0.00948143
Impeller Torque (n-m) 0.00037357
Impeller Power (W) 0.00469445
Working Volume =0.002 m
3
Correlations
Single or Multiple Impellers
Number of Impellers 2
Blend Time (s) 13.3723
Single Impeller
Impeller Type Radial
Reynolds Number 248.573
Froude Number 0.0187
Power Draw (w) 0.0093
Flow Rate (m3/s) 0.0002
246
Table C-5. Power draw and correlation for 15 % Solka Floc fermentation broth
(?=0.0775, RPM=120).
Impeller Type (for power draw calculations)
b
bb
p
N
D
W
aN ?
?
?
?
?
?
?
?
?
?
?
?
=
62.0
W
b
(m) 0.0092 a 5
N
b
6 b 0.8
D(m) 0.046
Power Draw
53
DNNP
p
?=
? (kg/m
3
) 1183.6 N
p
5
N (rps) 2 P(W) 0.0097
D(m) 0.046 ?(Kg/m?s) 0.0775
Report Torque (Top Shaft)
Upper Impeller
Shaft Torque (n-m) 0.00102215
Shaft Power (W) 0.01284475
Impeller Torque (n-m) 0.00051455
Impeller Power (W) 0.00646603
Lower Impeller
Shaft Torque (n-m) 0.00102215
Shaft Power (W) 0.01284475
Impeller Torque (n-m) 0.00050760
Impeller Power (W) 0.00637872
Working Volume =0.002 m
3
Correlations
Single or Multiple Impellers
Number of Impellers 2
Blend Time (s) 13.3723
Single Impeller
Impeller Type Radial
Reynolds Number 64.503
Froude Number 0.0187
Power Draw (w) 0.0097
Flow Rate (m3/s) 0.0002
247
Table C-6. Power draw and correlation for 20 % Solka Floc Fermentation Broth
(?=0.1050, RPM=120).
Impeller Type (for power draw calculations)
b
bb
p
N
D
W
aN ?
?
?
?
?
?
?
?
?
?
?
?
=
62.0
W
b
(m) 0.0092 a 5
N
b
6 b 0.8
D(m) 0.046
Power Draw
53
DNNP
p
?=
? (kg/m
3
) 1250.6 N
p
5
N (rps) 2 P(W) 0.0103
D(m) 0.046 ?(Kg/m?s) 0.1050
Report Torque (Top Shaft)
Upper Impeller
Shaft Torque (n-m) 0.00117487
Shaft Power (W) 0.01476383
Impeller Torque (n-m) 0.00059345
Impeller Power (W) 0.00745749
Lower Impeller
Shaft Torque (n-m) 0.00117487
Shaft Power (W) 0.01476383
Impeller Torque (n-m) 0.00058142
Impeller Power (W) 0.00730634
Working Volume =0.002 m
3
Correlations
Single or Multiple Impellers
Number of Impellers 2
Blend Time (s) 13.3723
Single Impeller
Impeller Type Radial
Reynolds Number 50.3044
Froude Number 0.0187
Power Draw (w) 0.0103
Flow Rate (m3/s) 0.0002