Prediction of coal calorific value based on the RBF neural network optimized by genetic algorithm

Abstract

The calorific value of coal is an important factor for the economic operation of coal fired power plant. However calorific value is tremendous difference between the different coal, and even if coal is from the same mine. Restricted by the coal market, most of coal fired power plants can not burn the designed-coal by now in China. The properties of coal as received are changing so frequently that pulverized coal firing is always with the unexpected condition. Therefore, the researches on the on-line prediction of calorific value of coal has a profound significance for the economic operation of power plants. Aiming at the problem of uncertainty of calorific value of coal, a soft measurement model for calorific value of coal is proposed based on the RBF neural network. And combined with the thought of k-cross validation, the genetic algorithm constructed a fitness function to optimize the RBF network parameters. It is shown by an example that the optimized model is concise and accurate, with good training accuracy and generalization ability. The model could provide a good guidance for the calculation of the calorific value of coal and optimization operation of coal fired power plants.