Efficient hybrid multiobjective optimization of pressure swing adsorption

Zhimian Hao,Adrian Caspari,Artur M Schweidtmann,Yannic Vaupel,Alexei A Lapkin,Adel Mhamdi

doi:10.1016/j.cej.2021.130248

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.

Highlights

Pressure swing adsorption (PSA) is an energy-efficient gas separation technology [1,2,3] that has been widely used in the industry for drying [4], air separation [5,6], and hydrogen production [7,8]
We discuss the optimization results after 50, 100, 200, 300, 400, 500, and 600 PSA simulations, which were recommended by TSEMO
This result might be explained in two ways: one explanation is that the estimated Pareto front is almost close to the actual Pareto front and leaves little space for further improvement; an alternative explanation is

Summary

Introduction

Pressure swing adsorption (PSA) is an energy-efficient gas separation technology [1,2,3] that has been widely used in the industry for drying [4], air separation [5,6], and hydrogen production [7,8]. PSA possesses significant advantages over the conventional amine-based CO2 capture technology with regards to emissions to the environment and energy consumption [3,11]. The optimal design and operation of PSA processes are challenging tasks due to the inherent cyclic and dynamic behavior of the system and highly nonlinear process models [12]. Since the column pressure varies over time, the PSA process can never reach a steady-state operating point. It eventually comes to a cyclic steady state (CSS), where the trajectories of state variables are the same for consecutive cycles

Results

Discussion

Conclusion