Artificial neural network-based prediction of hydrogen content of coal in power station boilers

In the present work, network modelling was performed using MATLAB with the Levenberg-Marquardt algorithm. Nine-hundred and three sets of data from a diverse range of coals have been used to develop the neural network architecture and topology. Trials were performed using one or two hidden layers with the number of neurons varied from 4 to 30. Validation data has been adopted to evaluate each trial and better model structure is determined to combat the over-fitting problem. As a result, it was found that a 4-12-1 or 4-8-4-1 network could give the most accurate prediction for this particular study. The regression analysis of the model tested gave a 0.937 correlation coefficient and the mean squared error of 0.0087. The average relative error is 5.46%. This has demonstrated that artificial neural networks have good potential for predicting elemental content of coal from frequently available proximate analysis data in power utilities.