MO-MCS, a Derivative-Free Algorithm for the Multiobjective Optimization of Adsorption Processes

Cyclic adsorption processes for gas separation, such as pressure and temperature swing adsorption (PSA and TSA), are nonstationary multicolumn processes. Their design involves many degrees of freedom, which offers a very high flexibility while calling for a systematic and rigorous optimization approach. As an additional challenge, optimization of these separation processes involves multiple objectives, e.g., minimal energy demand and maximal productivity, which have to be pursued while fulfilling given process specifications, e.g., purity and recovery of the target components. This work extends the multilevel coordinate search (MCS), a well-known model-based derivative-free algorithm, to constrained multiobjective problems. The algorithm, called MO-MCS, combines a built-in parallel computing strategy with the use of look-up tables with the goal of minimizing the computational time needed to determine the Pareto curve. The comparison with state-of-the-art optimizers indicates that MO-MCS shows better performance in terms of optimality, well spacing, and extension of the Pareto curve. Afterward, two industrially relevant case studies (TSA for CO2 separation and PSA for H2 and CO2 coproduction) are tackled to demonstrate the effectiveness of the algorithm as a tool to guide the design of adsorption processes.