Info!
UPDATED 1 Sept: The EI library in London is temporarily closed to the public, as a precautionary measure in light of the ongoing COVID-19 situation. The Knowledge Service will still be answering email queries via email , or via live chats during working hours (09:15-17:00 GMT). Our e-library is always open for members here: eLibrary , for full-text access to over 200 e-books and millions of articles. Thank you for your patience.

Combustion optimisation of stoker fired boiler plant by neural networks. Thai, S.M.; Wilcox, S.J.; Chong, A.Z.S.; Ward, J. Journal of the Energy Institute, Volume 81, Number 3, September 2008 , pp. 171-176(6)

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.
Please login to save this item