The work described in this paper aims to address the development of a Neural Network Based Controller (NNBC) to control chain grate stoker fired boilers. Artificial Neural Networks (ANNs) were used to estimate future emissions from and control the combustion process. The resultant ANNs were able to characterise the dynamics of the process and delivered rational multi-step-ahead predictions wth test data collected at an industrial chain grate stoker at HM Prison Garth, Lancashire. This technique was built into a carefully designed control strategy, to control the industrial stoker. Test results showed that the developed NNBC was able to optimise the industrial stoker boiler plant whilst delivering the load demand required and in so doing, the NNBC also managed to maintain low pollutant emissions.
- Design Engineering Division and Computers and Information in Engineering Division
Neural Network Modeling and Control of Stoker-Fired Boiler Plant
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Thai, SM, Wilcox, SJ, Chong, AZS, & Ward, J. "Neural Network Modeling and Control of Stoker-Fired Boiler Plant." Proceedings of the ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Volume 1: 21st Biennial Conference on Mechanical Vibration and Noise, Parts A, B, and C. Las Vegas, Nevada, USA. September 4–7, 2007. pp. 575-582. ASME. https://doi.org/10.1115/DETC2007-35228
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