Solvents have a significant impact on the efficiency and performance of chemical production. A large amount of solvent disposed after chemical production negatively impacts the environment and the economy. This study presents a solvent selection method based on a conceptual process design, which includes solvent processes used in chemical synthesis and waste solvent treatment. The proposed method is demonstrated with Suzuki–Miyaura coupling as a model reaction. The results show that solvent recycling costs and CO₂ emissions were lower than those observed for incineration. Importantly, if the appropriate solvent is selected, the cost and CO₂ emissions can be reduced by up to 67% and 70%, respectively, even with similar yields in the reaction. These results suggest that solvent selection combined with conceptual process design is the key to developing sustainable chemical production.

Propylene is industrially produced in a mixture with propane and generally separated from the mixture via distillation. However, because distillation is an energy-consuming process, a more efficient separation process should be developed to mitigate both carbon dioxide (CO₂) emissions and production costs. In this study, a two-stage membrane-separation process was designed, and its CO₂ emission and production costs were evaluated. The separation processes were designed to minimize energy consumption using different membrane combinations (two recently developed membranes each). To evaluate the separation processes using various membrane combinations, two indicators, i.e., CO₂ emissions and total annual costs (TACs), were estimated based on the process simulation (Pro/II, version 10.1.1) results, including energy consumptions, operation expenditure, and capital expenditure. These results were compared to the distillation processes as benchmarks, and the advantages of the membrane-separation process were discussed. In the comparison, carbon taxes were implemented for assessing these two independent indicators as a single indicator, i.e., TAC with carbon tax. Furthermore, using the same scheme, model membranes were also employed in the two-stage membrane-separation process as case studies of technological forecasts.

Various factors need to be considered in process design optimization to implement the complex processes of CO₂ capture, utilization, and storage (CCUS). Here, bi-objective optimization of single-stage CO₂ membrane separation was performed for two evaluation indexes: cost and CO₂ emissions. During optimization, the process flow configuration was fixed, the membrane performance was set under the condition of the Robeson upper bound, and the membrane area and operating conditions were set as variables. Bi-objective optimization was performed using an original algorithm that combines the adaptive design of experiments, machine learning, a genetic algorithm, and Bayesian optimization. Five case studies with different product CO₂ purities in the constraint were analyzed. Pareto solutions were superior for case studies with lower product CO₂ purities. The set of Pareto solutions revealed opposite directions for optimization: either (1) increase the membrane area to reduce CO₂ emissions but increase costs or (2) increase power consumption and reduce costs but increase CO₂ emissions. The implemented bi-objective optimization approach is promising for evaluating the membrane CO₂ capture process and the individual processes of CCUS.