A Neural Network Model for Predicting N0_x at the Melnik 1 Coal-powder Power Plant

摘要

This paper presents a non-conventional dynamic neural network that was designed for real time prediction of NO_x at the coal powder power plant Melnik 1, and results on real data are shown and discussed. The paper also presents the signal preprocessing techniques, the input-reconfigurable architecture, and the learning algorithm of the proposed neural network, which was designed to handle the non-stationarity of the burning process as well as individual failures of the measured variables. The advantages of our designed neural network over conventional neural networks are discussed.