Shuffled Mutation Glowworm Swarm Optimization and Its Application

Wang, Hongbo; Ren, Xuena; Tu, Xuyan

doi:10.1049/cje.2019.05.009

Cited by 2 publications

(1 citation statement)

References 15 publications

(21 reference statements)

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“…In the experiments on two real CFBBs, the proposed model accurately predicted the bed temperature. Booth and Roland [180] optimized boiler operation by using online learning neural networks, adjusted fuel quality fluctuations, and improved operational flexibility according to equipment performance changes caused by wear and maintenance activities. In addition, some boiler combustion optimization techniques have been applied at power plant sites.…”

Section: Algorithmmentioning

confidence: 99%

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

Liang¹,

Guo²,

Chen³

et al. 2023

CAAI Artificial Intelligence Research

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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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Section: Algorithmmentioning

confidence: 99%