Modeling and Optimization of Novel Fuel Production Strategies
Type of Degreedissertation
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Environmental problems such as global warming and fossil fuel shortage are some of the biggest challenges that human beings are facing nowadays. Alternative fuels are the potential answer to many future energy needs and current environmental concerns. A polygeneration plant is a complex system that can produce multiple products through different processing routes. The study of novel alternative fuel production through optimal processing strategies is the key to solving worldwide energy and environmental problems in a more efficient and cost-saving way. The objective of this research is primarily focused on the development of methodologies that integrate simulation, modeling and optimization tools for evaluation of the economic/environmental potentials of polygeneration facilities. The production of hydrogen and Fischer-Tropsch fuels are presented as case studies. Hydrogen is a “clean” energy source. The only product from the combustion of hydrogen is water, leaving zero carbon footprints. Fischer-Tropsch fuels, on the other side, can be matched directly to the fuel market. Therefore, case studies of different hydrogen production schemes and a comparison between traditional and novel FT fuel production processes have been developed to illustrate the methodology. This work successfully compared reformation strategies based on the impact of utility requirements, energy integration potential, equipment costs, and raw material costs on the total production cost. Meanwhile, different production scenarios of alternative fuels such as Fischer-Tropsch fuels were investigated. A comparison between the traditional gas phase Fischer-Tropsch process and the novel supercritical phase Fischer-Tropsch process was made. The results could lead us to a more efficient and environmental friendly alternative answer to satisfy many of the future energy needs. In addition, a novel method has been developed to optimize complex process networks. Disjunctive-Genetic Programming (D-GP), which is based on the integration of Genetic Algorithm (GA) with the disjunctive formulations of the Generalised Disjunctive Programming (GDP) for optimization of process networks, has been developed. This proposed approach eliminates the need for reformulation of the discrete/discontinuous optimization problems into direct MINLP problems, thus allowing for the solution of the original problem as a continuous optimization problem but only at each individual discrete and reduced search space. This method was used to optimize the selection process for complex products and production routes.