Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system

Li Wenlu; Guo Nan; Shang Tiewei; Sun Yueyang; Li Dapeng; Gao Xiaolong; Xiong Wei; Qiao Junfei

doi:10.1016/j.cjche.2025.09.028

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Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system

Updated：2026-05-14

- Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system
- Chinese Journal of Chemical Engineering Vol. 90, Issue 2, Pages: 168-180(2026)
- Affiliations：
  
  Information Science Department, Beijing University of Technology, Beijing 100024, China
  College of Carbon Neutrality Future Technology, Beijing University of Technology, Beijing 100124, China
  School of Electrical Engineering, Liaoning University of Technology, Jinzhou 121001, China
  Baotou Reclaimed Water Resources and Sewage Treatment Co., Ltd., Baotou 014000, China
  Baotou Water Group, Baotou 014000, China
- Author bio：
  
  Corresponding author. E-mail address: adqiao@bjut.edu.cn (J. Qiao).
- Funds：
- DOI：10.1016/j.cjche.2025.09.028
  CLC：
- Received：27 March 2025，
  
  Revised：2025-08-28，
  
  Accepted：01 September 2025，
  
  Online First：22 November 2025，
  
  Published：2026-02
- Accepted：
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Li Wenlu, Guo Nan, Shang Tiewei, et al. Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system[J]. Chinese Journal of Chemical Engineering, 2026, 90(2): 168-180.
DOI：

Li Wenlu, Guo Nan, Shang Tiewei, et al. Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system[J]. Chinese Journal of Chemical Engineering, 2026, 90(2): 168-180. DOI： 10.1016/j.cjche.2025.09.028.

摘要

Abstract

Coal gasification technology plays a pivotal role in chemical production as a key process for efficiently converting coal into liquid fuels and chemical feedstocks. During gasification

high-temperature reactions generate syngas

and optimizing its operational parameters is essential for improving syngas quality

carbon efficiency and liquid fuel yield. However

the intricate chemical reactions and heat transfer mechanisms in gasification necessitate costly simulations or experimental testing

making it an expensive multi-objective optimization problem. To address this challenge

this paper proposes a Knee Point-guided Heterogeneous Surrogate-assisted Evolutionary Algorithm (KG-HSEA) that integrates Kriging and Feedforward Neural Networks (FNN) to construct a heterogeneous surrogate model

leveraging their complementary strengths to reduce computational costs while maintaining predictive accuracy. By incorporating a knee point-guided search mechanism

the method prioritizes solutions that embody critical trade-offs among conflicting objectives. Moreover

an adaptive sampling strategy combined with dual-archive management is employed to dynamically update the surrogate model

ensuring it adapts to unstable operating conditions while maintaining robust convergence-diversity balance in coal gasification processes. Experimental results show that KG-HSEA achieved a 71.9% superiority rate with 23 optimal solutions out of 32 benchmark problems

highlighting its potential for efficient and feasible coal gasification optimization.

关键词

Keywords

references

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