
FOLLOWUS
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
Corresponding author. E-mail address: adqiao@bjut.edu.cn (J. Qiao).
收稿:2025-03-27,
修回:2025-08-28,
录用:2025-09-01,
网络首发:2025-11-22,
纸质出版:2026-02
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Li Wenlu, Guo Nan, Shang Tiewei, 等. Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system[J]. 中国化学工程学报(英文), 2026,90(2):168-180.
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.
Li Wenlu, Guo Nan, Shang Tiewei, 等. Knee point-guided heterogeneous surrogate-assisted optimization for multi-objective coal gasification system[J]. 中国化学工程学报(英文), 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:
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.
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