Efficient hybrid multiobjective optimization of pressure swing adsorption

Chemical Engineering Journal, vol. 423, pp. 130248

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.

Authors 6

1. Zhimian Hao

   University of Cambridge

   Affiliation as printed

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK

2. Adrian Caspari Aachen Aachener Verfahrenstechnik - Process Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   Aachener Verfahrenstechnik - Process Systems Engineering, RWTH Aachen University, Aachen 52062, Germany

3. Artur M. Schweidtmann Aachen Aachener Verfahrenstechnik - Process Systems Engineering

   RWTH Aachen University · Delft University of Technology

   Affiliation as printed

   Aachener Verfahrenstechnik - Process Systems Engineering, RWTH Aachen University, Aachen 52062, Germany

   Department of Chemical Engineering, Delft University of Technology, Van der Maasweg 9, Delft 2629 HZ, the Netherlands

4. Yannic Vaupel Aachen Aachener Verfahrenstechnik - Process Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   Aachener Verfahrenstechnik - Process Systems Engineering, RWTH Aachen University, Aachen 52062, Germany

5. Alexei A. Lapkin corresponding

   University of Cambridge · The Cambridge Centre for Advanced Research and Education in Singapore

   Affiliation as printed

   Cambridge Centre for Advanced Research and Education in Singapore Ltd, 1 Create Way, CREATE Tower #05-05, 138602, Singapore

   Department of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, UK

6. Adel Mhamdi corresponding Aachen Aachener Verfahrenstechnik - Process Systems Engineering

   RWTH Aachen University

   Affiliation as printed

   Aachener Verfahrenstechnik - Process Systems Engineering, RWTH Aachen University, Aachen 52062, Germany