A PERFORMANCE COMPARISON OF DIFFERENT BACK PROPAGATION NEURAL NETWORKS FOR NITROGEN OXIDES EMISSION PREDICTION IN THERMAL POWER PLANT

The use of Neural Networks (NN) has been proved to be a cost-effective technique. It is very important to choose a suitable back propagation (BP ) algorithm for training a neural network. While these algorithms prove to be very effective and robust in training many types of non-linear multivariable modeling, they suffer from certain disadvantages such as easy entrapment and very slow convergence. The power generating industry is undergoing an unprecedented reform. NN is applied to predict coal properties, economic load dispatch, emission prediction, temperature control, etc. This paper compares the performance of the six neural network methods to predict nitrogen oxides emission from a 500 MW coal fired thermal power plant.