Reaction synthesis, hybrid & intensified techniques assisted efficient process synthesis
Type of DegreePhD Dissertation
Restriction TypeAuburn University Users
MetadataShow full item record
Process synthesis is a crucial aspect of sustainable process design, aiming to identify efficient interconnections among various unit operations to convert raw materials into products. However, existing research predominantly focuses on downstream separation without considering the upstream reaction pathway selection and the application of innovative separation techniques. This dissertation addresses this gap by analyzing the process performance of selected innovative separation techniques and integrating them into process synthesis, thereby identifying optimal reaction pathways and novel process configurations for the entire process system, from reaction to separation. Two approaches were used to solve the synthesis problem. The first approach employed a decomposed synthesis approach, where the problem is decomposed into three sub-problems: generation/selection of reaction pathways, generation/selection of separation configurations, and results analysis. To address the generation/selection of reaction pathways, the reaction synthesis tool ASKCOS was utilized to generate potential feasible pathways based on given reactants or products. Selection criteria, encompassing reaction performance, and reactant/product properties (such as reaction enthalpy, toxicity, profitability, and ease of separation), were proposed for pathway screening and selection. The selected top pathways were then subjected to the generation of separation configurations using a thermodynamic insight-based method. Feasible separation techniques were identified based on component thermodynamic properties. To identify the optimal solvent for azeotrope mixture separation, a solvent evaluation method was introduced. A validation model based on derivative-free optimization (DFO) was developed to validate the solvent selection results, demonstrating the effectiveness of the developed solvent selection model for agent-based distillation. Furthermore, prior to integrating advanced separation techniques into the configurations, a hybrid distillation design was performed to evaluate the performance of these techniques using the developed DFO model. Results showed that applying hybrid distillation to separate azeotrope mixtures could reduce solvent usage and process energy cost, thereby enhancing overall process sustainability. However, the effectiveness of hybrid distillation, such as distillation membrane/adsorption, strongly depends on the membrane/adsorbent separation abilities. Hybrid design results for simple mixtures indicated potential benefits in terms of reducing separation energy cost and/or improving process throughput. The proposed solvent selection model and hybrid distillation design contribute to a better understanding of innovative separation techniques and assist the generation/selection of process configurations. Subsequently, rigorous simulation and life-cycle analysis were performed to analyze the selected process flowsheets. By comparing utility cost, capital cost, carbon footprint, and global warming potential (GWP) among different processes, the optimal sustainable process flowsheet was determined. The second approach involved optimization, which simultaneously determined the optimal pathway and flowsheet instead of solving sub-problems step-by-step. A modified generalized distillation network optimization model, considering different reaction pathways and process carbon emissions, was employed and integrated with additional thermodynamic databases to determine the optimal process route. By inputting a list of potential reaction pathways and their corresponding conversion rate/selectivity, the optimization model extracted thermodynamic data from the database and performed optimization for multi-step reaction pathways, starting from raw materials and intermediates to the final product. Therefore, the optimal process route was determined by comparing the objective values of each pathway.