E3S Web Conf.
Volume 174, 2020Vth International Innovative Mining Symposium
|Number of page(s)||7|
|Section||Innovations in Mining Machinery|
|Published online||18 June 2020|
Development and Performance Evaluation of a Computer Program Based on Neural Network Mathematical Models for Forecasting By- Product Yield
1 T.F. Gorbachev Kuzbass State Technical University, 650000, Russian Federation
2 Koks PJSC, 6 1st Stakhanovkaya, Kemerovo, 650021, Russian Federation
Process performance of coking plants are based on data on the yield of by-products of coking coal and their quality, therefore, much attention is paid to the issues of their analysis. In view of the complexity and insufficient knowledge of the relationship between these parameters, mathematical modeling of this dependence using neural networks is of great interest. Based on a mathematical analysis of experimental data on the quality indicators of coal, coal concentrates and the by-product yield, neural network mathematical models have been developed to forecast the parameters under study. The neural network is based on the Ward’s network. Based on the results of the research, the application “Intelligent Information System for Forecasting By-product Yield” was created, which implements neural networks . The relative forecasting error for the parameter “coke” is 0.64±0.23%, “coal tar” is 19.53±5.25%, “crude benzene” is 10.02±2.83%, and “coke gas” is 5.11±1.34%. A comparative analysis of the data obtained using the developed design method is carried out, with the simulation results using existing methods, as well as with the production values of by-products yield.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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