Bi-Objective Optimization of Techno-Economic and Environmental Performance of CO Capture Strategy Involving Two-Stage Membrane-Based Separation with Recycling.

To effectively implement complex CO 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 CO 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 CO 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*(CO/N), 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 CO emissions. The results of the case studies quantitatively clarify that improving the α*(CO/N) 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 CO separation and individual CCUS processes.