Coking energy consumption radial basis function prediction model improved by differential evolution algorithm

Abstract

This paper presents a radial basis function prediction model improved by differential evolution algorithm for coking energy consumption process, which is very difficult to get online and real time because of the complex process. In the energy consumption prediction model, target flue temperature, flue suction, water content, volatile coal and coking time are considered as input variables, and coking energy consumption as output variables. To overcome the shortcomings of radial basis function network, such as poor learning ability and slow convergence speed, the energy consumption prediction model optimized by differential evolution algorithm is improved. Using the strong global search ability of differential evolution algorithm, the center value, width and output weight of the basis function in radial basis function network is obtained by differential evolution algorithm. Then the optimal values are taken as the center value, width and output weight of the of radial basis function neural network. The results show that the improved radial basis function prediction has higher accuracy, stability and training speed of the network. The radial basis function prediction model has great significance in reducing coking energy consumption, saving enterprise costs, increasing coke production and improving enterprise economic benefits.