Surrogate-assisted multi-objective particle swarm optimization for the operation of CO2 capture using VPSA

Khalil Alkebsi and Wenli Du

Energy, 2021, vol. 224, issue C

Abstract: Multi-objective evolutionary algorithms (MOEAs) have received increasing attention over the past few decades. However, when applying MOEAs to solve computationally expensive real-world applications, they are often criticized due to the large number of function evaluations required. To this end, we proposed a method, called MOPSONN-EGO, for dealing with expensive multi-objective problems. MOPSONN-EGO uses Kriging models to approximate the objective function of expensive problems. The recently proposed MOPSONN algorithm is used to search the landscape of the Kriging models. The cheap-to-evaluate expected improvement matrix is adopted to select infill samples to update the Kriging models. Empirical results on several benchmark problems show the competitive performance of MOPSONN-EGO. Moreover, MOPSONN-EGO is applied to solve the multi-objective optimization problem of the Vacuum pressure swing adsorption (VPSA) for carbon dioxide capture. The simulation results are validated against the expensive model using normalized root mean square error (NRMSE) and compared to the One-shot surrogate modeling strategy. The results reveal the significant efficiency of the proposed algorithm in approximating the real Pareto front of VPSA using only a limited number of detailed model evaluations. Moreover, the obtained best values for each objective with their corresponding decision variables values are reported.

Keywords: Carbon capture; Vacuum pressure swing adsorption (VPSA); Multi-objective swarm optimization (MOPSO); Surrogate-assisted optimization (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544221003273
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:224:y:2021:i:c:s0360544221003273

DOI: 10.1016/j.energy.2021.120078

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().