首页|Optimization of coal gasification process based on a dynamic model management strategy

Optimization of coal gasification process based on a dynamic model management strategy

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  • NSTL
  • Elsevier
Owing to the complex physical and chemical reactions, optimization of the coal gasification process requires a large computational cost. In chemical engineering design and optimization, the surrogate model is often used to assist the evolutionary algorithm (EA) in solving computationally expensive problems. In this paper, a dynamic model management strategy based on adaptive surrogate selection (ASS) is proposed to identify an appropriate surrogate model to assist the particle swarm optimization (PSO) algorithm faced with complex problems. In ASS, based on the given dataset, a right surrogate model was selected from the diversity model library by adopting the minimum root mean square error and K-fold cross-validation before assisting PSO to identify an optimal solution, and reselected constantly with the addition of a new sample. Based on the ASS, in addition to the adaptive global model, the local model is introduced and selected adaptively to refine the optimal solution more rapidly, then switched dynamically with the global model. The most uncertain sample was also considered for searching the unexplored region and escaping from the local optimum. Comparison of the specific surrogate EAs and three other state-of-the-art surrogate-assisted EAs revealed that the proposed strategy significantly improved the optimization performance of PSO and the effective syngas yield of the coal gasification process. (C) 2021 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Dynamic model management strategyAdaptive surrogate selectionLocal modelParticle swarm optimizationCoal gasification process optimizationCONSTRAINED GLOBAL OPTIMIZATIONSURROGATE MODELSPARTICLE SWARMEVOLUTIONARYSIMULATIONFRAMEWORKENSEMBLEDESIGNFEASIBILITYALGORITHM

Geng, Yang、Zhang, Zhihua、Bai, Jinfeng、Li, Shaojun、Liu, Yang、Li, Chao、Zhong, Xiangyun

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Univ Sci & Technol Liaoning

East China Univ Sci & Technol

2022

10.1016/j.jtice.2021.104185

Journal of the Taiwan Institute of Chemical Engineers

Journal of the Taiwan Institute of Chemical Engineers

EISCI
ISSN：1876-1070
年,卷(期)：2022.131
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