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

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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：
  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：

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.

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Keywords

references

M. Wiatowski, W. Basa, M. Pankiewicz-Sperka, M. Szyja, H.R. Thomas, R. Zagorscak, S. Sadasivam, S. Masum, T. Kempka, C. Otto, K. Kapusta, Experimental study on tar formation during underground coal gasification: Effect of coal rank and gasification pressure on tar yield and chemical composition, Fuel 357 (2024) 130034.

R.Q. Mu, M. Liu, J.J. Yan, Advanced exergy analysis on supercritical water gasification of coal compared with conventional O 2 -H 2 O and chemical looping coal gasification, Fuel Process. Technol. 245 (2023) 107742.

Z. Shen, Z.Y. Huang, High-efficiency and pollution-controlling in-situ gasification chemical looping combustion system by using CO 2 instead of steam as gasification agent, Chin. J. Chem. Eng. 26 (11) (2018) 2368—2376.

Y. Cui, J. Liang, Z.Q. Wang, X.C. Zhang, C.Z. Fan, D.Y. Liang, X. Wang, Forward and reverse combustion gasification of coal with production of high-quality syngas in a simulated pilot system for in situ gasification, Appl. Energy 131 (2014)9—19.

T.H. Duan, C.P. Lu, S. Xiong, Z.B. Fu, B. Zhang, Evaluation method of the energy conversion efficiency of coal gasification and related applications, Int. J. Energy Res. 40 (2) (2016) 168—180.

X.Y. Yao, W.H. Li, X.G. Pan, R. Wang, Multimodal multi-objective evolutionary algorithm for multiple path planning, Comput. Ind. Eng.169(2022)108145.

F. Xie, M. An, P. Li, X.D. Hu, H.C. Bai, Q.J. Guo, Simulation study on the gasification process of Ningdong coal with iron-based oxygen carrier, Chin. J. Chem. Eng. 29 (2021) 326—334.

L. Huang, L. Qi, H.N. Wang, J.L. Zhang, X.Q. Jia, Optimal design of heat exchanger header for coal gasification in supercritical water through CFD simulations, Chin. J. Chem. Eng. 25(8)(2017)1101—1108.

S.S. Ye, Q. Tang, Y.D. Wang, W.Y. Fei, Structural optimization of a settler via CFD simulation in a mixer-settler, Chin. J. Chem. Eng. 28 (4) (2020) 995—1015.

J.P. Luo, Y.F. Dong, Q.Q. Liu, Z.X. Zhu, W.M. Cao, K.C. Tan, Y.C. Jin, A new multitask joint learning framework for expensive multi-objective optimization problems, IEEE Trans. Emerg. Top. Comput. Intell. 8 (2) (2024) 1894—1909.

B.D. Li, Y.T. Yang, D.C. Liu, Y. Zhang, A.M. Zhou, X. Yao, Accelerating surrogate assisted evolutionary algorithms for expensive multi-objective optimization via explainable machine learning, Swarm Evol. Comput. 88 (2024) 101610.

S. Kuhnt, D.M. Steinberg, Design and analysis of computer experiments, Asta Adv. Stat. Anal.94 (4)(2010)307—309.

L.Q. Pan, C. He, Y. Tian, H.D. Wang, X.Y. Zhang, Y.C. Jin, A classification-based surrogate-assisted evolutionary algorithm for expensive many-objective optimization, IEEE Trans. Evol. Comput.23(1)(2019)74—88.

Y. Tian, J.X. Hu, C. He, H.P. Ma, L.M. Zhang, X.Y. Zhang, A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multiobjective optimization, Swarm Evol. Comput. 80 (2023) 101323.

X.L. Wang, Y.C. Jin, S. Schmitt, M. Olhofer, An adaptive Bayesian approach to surrogate-assisted evolutionary multi-objective optimization, Inf. Sci. 519 (2020)317—331.

N.V. Queipo, R.T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan, P. Kevin Tucker, Surrogate-based analysis and optimization, Prog. Aerosp. Sci. 41 (1) (2005) 1—28.

C.L. Hua, Y.C. Cui, F. Wu, R.D. Zhang, Enhanced process monitoring for industrial coking furnace using a dual-channel pooling and homologous bilinear model-based convolutional neural network, Can. J. Chem. Eng.102 (8) (2024) 2857—2875.

E. Lughofer, R. Pollak, C. Feilmayr, M. Schatzl, S. Saminger-Platz, Prediction and explanation models for hot metal temperature, silicon concentration, and cooling capacity in ironmaking blast furnaces, Steel Res. Int.92(9)(2021) 2100078.

J.Q. Lin, C. He, R. Cheng, Adaptive dropout for high-dimensional expensive multiobjective optimization, Complex Intell, Syst. 8(1) (2022)271—285.

F. Li, L. Gao, W.M. Shen, Surrogate-assisted multi-objective evolutionary optimization with Pareto front model-based local search method, IEEE Trans. Cybern.54(1)(2024)173—186.

R. Snaiki, A. Jamali, A. Rahem, M. Shabani, B.L. Barjenbruch, A metaheuristicoptimization-based neural network for icing prediction on transmission lines, Cold Reg. Sci. Technol. 224 (2024) 104249.

J.Y. Zhang, A.M. Zhou, G.X. Zhang, A classification and Pareto domination based multiobjective evolutionary algorithm, IEEE, Sendai, Japan,2015.

Z.S. Song, H.D. Wang, H.B. Xu, A framework for expensive many-objective optimization with Pareto-based bi-indicator infill sampling criterion, Memet. Comput.14 (2)(2022)179—191.

X.Y. Zhang, Y. Tian, Y.C. Jin, A knee point-driven evolutionary algorithm for many-objective optimization, IEEE Trans. Evol. Comput. 19 (6) (2014) 761—776.

J. Zou, Q.Y. Li, S.X. Yang, H. Bai, J.H. Zheng, A prediction strategy based on center points and knee points for evolutionary dynamic multi-objective optimization, Appl. Soft Comput.61 (2017)806—818.

A. Konak, D.W. Coit, A.E. Smith, Multi-objective optimization using genetic algorithms: a tutorial, Reliab. Eng. Syst. Saf.91 (9)(2006)992—1007.

C.L. Park, B.G. Kim, The optimization of low-rank coal grinding for transport coal gasification by robust design, Fuel 95(2012) 282—286.

J. Sacks, W.J. Welch, T.J. Mitchell, H.P. Wynn, Design and analysis of computer experiments, Statist. Sci.4(4)(1989)433—435.

H. Jaeger, M. Lukosevicius, D. Popovici, U. Siewert, Optimization and applications of echo state networks with leaky-integrator neurons, Neural Netw. 20 (3) (2007) 335—352.

L. Rachmawati, D. Srinivasan, Multiobjective evolutionary algorithm with controllable focus on the knees of the Pareto front, IEEE Trans. Evol. Comput. 13(4)(2009)810—824.

J.F. Qiu, M.H. Liu, L. Zhang, W. Li, F. Cheng, A multi-level knee point based multi-objective evolutionary algorithm for AUC maximization, Memet. Comput.11 (3)(2019)285—296.

H.K. Chen, Y. Tian, W. Pedrycz, G.H. Wu, R. Wang, L. Wang, Hyperplane assisted evolutionary algorithm for many-objective optimization problems, IEEE Trans. Cybern. 50(7)(2019)3367—3380.

R.V. Lenth, Least-squares means: The R Packagelsmeans, J. Stat. Soft. 69 (1) (2016)1—33.

K. Deb, L. Thiele, M. Laumanns, E. Zitzler, Scalable multi-objective optimization test problems. Proceedings of the 2002 Congress on Evolutionary Computation, Honolulu, HI, USA, IEEE, 2002, pp. 825—830.

C.A. Coello Coello, M. Reyes-Sierra, Multi-objective particle swarm optimizers: a survey of the state-of-the-art, Int. J. Comput. Intell. Res. 2 (3) (2006) 287—308.

X.D. Kong, W.M. Zhong, W.L. Du, F. Qian, Compartment modeling of coal gasification in an entrained flow gasifier: a study on the influence of operating conditions, Energy Convers. Manag. 82 (2014) 202—211.

X.B. Shang, Z. Zhang, H. Fang, B. Li, Y.H. Li, Ensemble learning of multikernel Kriging surrogate models using regional discrepancy and spacefilling criteria-based hybrid sampling method, Adv. Eng. Inform. 58 (2023) 102186.

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