PREDICTING COAL ASH FUSION TEMPERATURE WITH A BACKPROPAGATION NEURAL-NETWORK MODEL

A novel technique, the back-propagation (BP) neural network, is presen ted for predicting the ash fusion temperature from ash compositions fo r some Chinese coals instead of the traditional techniques, such as th e ternary equilibrium phase diagrams and regression relationships. In the applications of the BP networks, some modifications to the origina l BP algorithm are adopted to speed up the BP learning algorithm, and some useful advice is put forward for the choice of some key parameter s in the BP model. Compared to the traditional techniques, the BP neur al network method is much more convenient and direct, and can always a chieve a much better prediction effect. (C) 1998 Elsevier Science Ltd All rights reserved.