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.

Prediction of Coal Calorific Value Based on a Hybrid Linear Regression and Support Vector Machine Model

“…It was observed that ash and moisture have negative effects on GCV. Research results show that there is a good correlation between GCV and coal ingredient, such as moisture and ash [6,7]. The paper regards moisture and ash as two independent variables, GCV as a dependent variable affected by the former two parameters.…”

Online identification of the lower heating value of the coal entering the furnace based on the boiler-side whole process models

“…101 In the literature, data-driven methods also have been published 102 to predict the heating value of coal using artificial nervous network 103 [25][26][27], fuzzy inference system [28], multiple regression method 104 [29], etc. Some of them need input the composition of the coal 105 through the proximate or ultimate analysis into the models 106 [25,28]; the others take several highly relevant process variables 107 as input, such as mass flow rates of the main steam and the reheat 108 steam, coal feeder speed, total air amount, etc. [26,27].…”