Bi-Objective Optimization of Techno-Economic and Environmental Performance for Membrane-Based CO2 Capture via Single-Stage Membrane Separation.

Abstract

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.