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CAAI Artificial Intelligence Research 2023, 2: 9150014 https://doi.org/10.26599/AIR.2023.9150014
Article | Open Access | Issue | Published: 30 June 2023

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

Show Author's Information Jing Liang1,2,3,  Hao Guo1,2,  Ke Chen1,2( ),  Kunjie Yu1,2,  Caitong Yue1,2,  Yunpeng Ma4
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471000, China
School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang 453000, China
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China
Keywords:
computational intelligence, intelligent optimization algorithm, boiler combustion optimization, circulating fluidized bed boiler, environmental protection
Cite this article:
Liang J, Guo H, Chen K, et al. A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization. CAAI Artificial Intelligence Research, 2023, 2: 9150014. https://doi.org/10.26599/AIR.2023.9150014
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Abstract Full text About this article

This paper reviews the researches on boiler combustion optimization, which is an important direction in the field of energy saving and emission reduction. Many methods have been used to deal with boiler combustion optimization, among which evolutionary computing (EC) techniques have recently gained much attention. However, the existing researches are not sufficiently focused and have not been summarized systematically. This has led to slow progress of research on boiler combustion optimization and has obstacles in the application. This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms. Finally, this paper discusses new research challenges and outlines future research directions, which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations.


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About this article

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

Show Author's information Jing Liang1,2,3, Hao Guo1,2, Ke Chen1,2( ), Kunjie Yu1,2, Caitong Yue1,2, Yunpeng Ma4
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China
State Key Laboratory of Intelligent Agricultural Power Equipment, Luoyang 471000, China
School of Electrical Engineering and Automation, Henan Institute of Technology, Xinxiang 453000, China
School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China

Abstract

This paper reviews the researches on boiler combustion optimization, which is an important direction in the field of energy saving and emission reduction. Many methods have been used to deal with boiler combustion optimization, among which evolutionary computing (EC) techniques have recently gained much attention. However, the existing researches are not sufficiently focused and have not been summarized systematically. This has led to slow progress of research on boiler combustion optimization and has obstacles in the application. This paper introduces a comprehensive survey of the works of intelligent optimization algorithms in boiler combustion optimization and summarizes the contributions of different optimization algorithms. Finally, this paper discusses new research challenges and outlines future research directions, which can guide boiler combustion optimization to improve energy efficiency and reduce pollutant emission concentrations.

Keywords: computational intelligence, intelligent optimization algorithm, boiler combustion optimization, circulating fluidized bed boiler, environmental protection

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Publication history
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Acknowledgements
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Publication history

Received: 19 December 2022
Revised: 23 February 2023
Accepted: 22 March 2023
Published: 30 June 2023
Issue date: December 2023

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© The author(s) 2023.

Acknowledgements

Acknowledgment

This work was supported by the National Natural Science Foundation of China (Nos. 61806179, 61876169, 61922072, 61976237, 61673404, 62106230, 62006069, 62206255, and 62203332), China Postdoctoral Science Foundation (Nos. 2021T140616, 2021M692920, 2022M712878, and 2022TQ0298), Key R&D Projects of Ministry of Science and Technology (No. 2022YFD2001200), Key R&D and Promotion Projects in Henan Province (Nos. 192102210098 and 212102210510), and Henan Postdoctoral Foundation (No. 202003019).

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