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.

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