Combustion Optimisation of Stoker-Fired Boiler Plant by Neural Networks

Steven Wilcox, John Ward, Shee Thai, Zyh Chong

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Pages (from-to)171 - 176
Number of pages5
JournalJournal of the Energy Institute
Issue number3
Publication statusPublished - 1 Sept 2008


  • combustion optimisation
  • chain grate stoker
  • artificial neural networks


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