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Efficient hybrid multiobjective optimization of pressure swing adsorption

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Hao, Z 
Caspari, A 
Schweidtmann, AM 
Vaupel, Y 
Lapkin, AA 

Abstract

Pressure swing adsorption (PSA) is an energy-efficient technology for gas separation, while the multiobjective optimization of PSA is a challenging task. To tackle this, we propose a hybrid optimization framework (TSEMO + DyOS), which integrates two steps. In the first step, a Bayesian stochastic multiobjective optimization algorithm (i.e., TSEMO) searches the entire decision space and identifies an approximated Pareto front within a small number of simulations. Within TSEMO, Gaussian process (GP) surrogate models are trained to approximate the original full process models. In the second step, a gradient-based deterministic algorithm (i.e., DyOS) is initialized at the approximated Pareto front to further refine the solutions until local optimality. Therein, the full process model is used in the optimization. The proposed hybrid framework is efficient, because it benefits from the coarse-to-fine function evaluations and stochastic-to-deterministic searching strategy. When the result is far away from the optima, TSEMO can efficiently approximate a trade-off curve as good as a commonly used evolutional algorithm, i.e., Nondominated Sorting Genetic Algorithm II (NSGA-II), while TSEMO only uses around 1/16th of CPU time of NSGA-II. This is because the GP-based surrogate model is utilized for function evaluations in the initial coarse search. When the result is near the optima, the searching efficiency of TSEMO dramatically decreases, while DyOS can accelerate the searching efficiency by over 10 times. This is because, in the proximity of optima, the exploitation capacity of DyOS is significantly higher than that of TSEMO.

Description

Keywords

4004 Chemical Engineering, 40 Engineering, 4016 Materials Engineering, 4011 Environmental Engineering, 7 Affordable and Clean Energy

Journal Title

Chemical Engineering Journal

Conference Name

Journal ISSN

1385-8947

Volume Title

423

Publisher

Elsevier BV
Sponsorship
National Research Foundation Singapore (via Cambridge Centre for Advanced Research and Education in Singapore (CARES)) (unknown)
Cambridge Trust CSC National Research Foundation Singapore