Bi-Objective Optimization of Techno-Economic and Environmental Performance of CO₂ Capture Strategy Involving...

To effectively implement complex CO₂ capture, utilization, and storage (CCUS) processes, it is essential to optimize their design by considering various factors. This research bi-objectively optimized a two-stage membrane-based separation process that includes recycling, concentrating on minimizing both costs and CO₂ emissions. The implemented algorithm combined experimental design, machine learning, genetic algorithms, and Bayesian optimization. Under the constraints of a recovery rate of 0.9 and a produced CO₂ purity of 0.95, six case studies were conducted on two types of membrane performance: the Robeson upper bound and a tenfold increase in permeability. The maximum value of α*(CO₂/N₂), used as a constraint, was adjusted to three levels: 50, 100, and 200. The analysis of the Pareto solutions and the relationship between each design variable and the final evaluation index indicates that electricity consumption significantly impacts operating costs and CO₂ emissions. The results of the case studies quantitatively clarify that improving the α*(CO₂/N₂) results in a greater enhancement of process performance than increasing the membrane's performance by increasing its permeability. Our bi-objective optimization analysis allowed us to effectively evaluate the membrane's CO₂ separation and individual CCUS processes.