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.
Highlights
Coke is the main raw material and fuel in the production of smelting, chemical and mechanical industries, and it is an indispensable material in industrial production
In the coking process, when the pyrolysis gas produced from the colloidal layer causes the moisture content in the coal to be less than 8%, no additional coking heat loss will be generated, but when higher than 8%, the water will have a greater impact on the coking heat, every 1% of the water changes, the corresponding coking heat will increase by 30 kJ/kg and coking energy consumption increases
In this paper, based on the complexity of coking production process and many influential factors, the difficulty of predicting model of coking energy consumption is presented, and the prediction model of coking energy consumption based on the Radial basis function (RBF) neural network is proposed by the Differential evolutionary algorithm (DE) algorithm
Summary
Coke is the main raw material and fuel in the production of smelting, chemical and mechanical industries, and it is an indispensable material in industrial production. When the volatile content is too high, it will increase the heat absorption in the reaction process, affect the normal production process of coke oven, and when the content is too low, it will make the push coke difficult, the production time increases, the coking energy consumption increases. Through the analysis of the influence factors of coking energy consumption, the temperature t1 of the cokeside target, the temperature of the t2, the water Md, The coking time T, the volatile Vdaf, the coke-side flue suction xl[1] and the suction xl[2] of the side flue are the input variables of the coking energy consumption prediction model. Ð7Þ where r1, r2, r3, r4, r5 represents a randomly generated natural number of differences that are not equal between [1, NP], and F is a mutation factor, which affects the amplification of the deviation variable
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
