Combustion optimisation of stoker fired boiler plant by neural networks

S M Thai; Steven Wilcox; A Z S Chong; J Ward

doi:10.1179/174602208X339131

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Combustion optimisation of stoker fired boiler plant by neural networks

Journal article

Peer reviewed

Combustion optimisation of stoker fired boiler plant by neural networks

S M Thai, Steven Wilcox, A Z S Chong and J Ward

Journal of the Energy Institute, Vol.81(3), pp.171-176

2008

DOI: https://doi.org/10.1179/174602208X339131

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Abstract

Artificial neural networks

Chain grate stoker

Combustion optimisation

This paper is concerned with the development of a neural network based controller (NNBC) for chain grate stoker fired boilers. The objective of the controller was to increase combustion efficiency and maintain pollutant emissions below future medium term legislation. Artificial neural networks (ANNs) were used to estimate future emissions from and control the combustion process. Initial tests at Casella CRE Ltd demonstrated the ability of ANNs to characterise the complex functional relationships which subsisted in the data set, and utilised previously gained knowledge to deliver multistep ahead predictions. This technique was built into a carefully designed control strategy, which fundamentally mimicked the actions of an expert boiler operator, to control an industrial chain grate stoker at HM Prison Garth, Lancashire. Test results demonstrated that the developed novel NNBC was able to optimise the industrial stoker boiler plant whilst keeping the excess air level to a minimum. In addition, the ANN also managed to maintain the pollutant emissions within possible future limits for boilers in the size range of 1 to 50 MW. This prototype controller would thus offer the industrial coal user a means to improve the combustion efficiency on chain grate stokers as well as meeting probable medium term legislation limits on pollutant emissions.

Details

Title: Combustion optimisation of stoker fired boiler plant by neural networks
Authors: S M Thai (Author) - University of South Wales
Steven Wilcox (Author) - University of Glamorgan
A Z S Chong (Author) - University of Nottingham
J Ward (Author)
Publication details: Journal of the Energy Institute, Vol.81(3), pp.171-176
Publisher: Elsevier Advanced Technology
Date published: 2008
DOI: 10.1179/174602208X339131
ISSN: 1746-0220; 1743-9671
Organisation Unit: Office of the Deputy Vice-Chancellor (Academic); University of the Sunshine Coast, Queensland
Language: English
Record Identifier: 99513806202621
Output Type: Journal article

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