Multi-objective optimization of supersonic separator for gas removal and carbon capture using three-field two-phase flow model and non-dominated sorting Genetic Algorithm-II (NSGA-II)

Ding, H. ORCID: https://orcid.org/0000-0002-0316-134X, Zhang, G., Wang, S., Zhang, Y., Yang, Y. ORCID: https://orcid.org/0000-0001-5796-7990 and Wen, C. ORCID: https://orcid.org/0000-0002-4445-1589 (2025) Multi-objective optimization of supersonic separator for gas removal and carbon capture using three-field two-phase flow model and non-dominated sorting Genetic Algorithm-II (NSGA-II). Separation and Purification Technology, 358. 130363. ISSN 1873-3794 doi: 10.1016/j.seppur.2024.130363

Abstract/Summary

Supersonic separator is an efficient technology for gas removal and carbon capture. To enhance its performance, many researchers have studied its structure; however, existing studies have primarily used traditional computational fluid dynamics (CFD) models for single-objective structural optimization of the separator’s separation performance. However, in the supersonic separators, separation efficiency and pressure-loss ratio are the most important and conflicting performance parameters, and evaluating separation performance in isolation from either one is incomplete. In the present study, we develop a gas–liquid two-phase three-field CFD model considering liquid films. This mathematical model is combined with the non-dominated Sorting Genetic Algorithm-II (NSGA-II) for multi-objective optimization of the coupled multiple structural parameters with the objective of the separation efficiency and pressure-loss ratio. The results indicate that the maximum relative errors between simulated and predicted values for the four Pareto optimal solutions in computing pressure loss ratio and separation efficiency are 5.4% and 5.3%, respectively. The optimized solutions achieve the maximum reduction in pressure loss ratio of 28.3% at the same 90% separation efficiency compared to the original structure.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/121810
Identification Number/DOI	10.1016/j.seppur.2024.130363
Refereed	Yes
Divisions	Science > School of the Built Environment > Construction Management and Engineering Science > School of the Built Environment > Energy and Environmental Engineering group
Publisher	Elsevier
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