A comparative study of multi-objective optimization with ANN-based VPSA model for CO2 capture from dry flue gas. Issue 3 (June 2022)

• Record Type: Journal Article
• Authors: Wang, Zhenguang; Shen, Yuanhui; Zhang, Donghui; Tang, Zhongli; Li, Wenbin
• Is Part Of: Journal of environmental chemical engineering. Volume 10:Issue 3(2022)
• Publication Date: 2022-06
• Subjects: Vacuum pressure swing adsorption -- Multi-objective optimization -- CO2 capture -- Artificial neural network -- Metaheuristic algorithm; Chemical engineering -- Environmental aspects -- Periodicals; Environmental engineering -- Periodicals; Chemical engineering -- Environmental aspects; Environmental engineering; Periodicals; 660.0286
• DOI: 10.1016/j.jece.2022.108031
• Languages: English
• ISSNs: 2213-2929

Abstract

Abstract: A 3-bed-7-step vacuum pressure swing adsorption (VPSA) process was developed for carbon capture from dry flue gas with a silica gel adsorbent. The VPSA process is simulated based on a series of detailed models. This paper aims to solve the multi-objective optimization problem of the VPSA process. A trained artificial neural network (ANN) model with double hidden layers was established to optimize the process performance through the metaheuristic algorithm (NSGA-II, MOPSO, MOEA/D, and NSGA-III). Subsequently, the diversity and convergence performance of the metaheuristic algorithm were analyzed and compared. The optimization results show that on the one hand, the purity of CO2 could reach 80.94% with recovery of 90.61%; and on the other hand, the productivity could reach 0.5233 mol/h/kg with energy consumption of 1004.14 kJ/kgCO2 with the constraint of 70% purity and 90% recovery. The results indicate that, the ANN model can predict the performance metrics and dynamic performance of the VPSA process with very high accuracy. Meanwhile, the NSGA-II can obtain a comprehensive set of trade-off alternatives from different optimization problems, which can be used as a powerful reference for the operation of the carbon capture process. Highlights: Rigorous models for carbon capture have been established. Precise artificial neural networks were trained as surrogate models. This study evaluated evolutionary many-objective optimization for VPSA. The performance of four many-objective optimization algorithms was compared. Performances of initial and optimal conditions are compared.