Authors: E.V. Vasileva, T.G. Cherkasova, A.V. Nevedrov, Papin , S.P. Sybbotin

Title of the article: Cluster analysis of results of experimental evaluation of output of chemical prodicts of coking

Year: 2019, Issue: 2, Pages: 87-95

Branch of knowledge: 05.17.07 Chemical technology of fuel and high-energy substances

Index UDK: 54.062:004.032.26

DOI: 10.26730/1999-4125-2019-2-87-95

Abstract: Modern computer technologies play an important role in the modernization of industrial enterprises, especially at the stage of assessing the quantitative indicators of the output of finished products, so the aim of this work is to develop a scientifically based method for predicting the output of chemical coking products from coal and their mixtures based on the characteristics of their quality using the method of neural network mathematical modeling. Quality indicators of coal and coal mixtures were determined by standard methods of technical, petrographic and elemental analysis, as well as sintering analysis. The yield of chemical products of coking was determined on the basis of GOST 18635-73 «Coals. Method for determining the yield of chemical products of coking». The article presents the results of cluster analysis of these indicators of coal quality and the yield of chemical products of coking on the example of coke, coal tar, crude benzene and coke oven gas. This method of research is part of the mathematical analysis of data and is necessary for subsequent mathematical modeling. Based on the analysis of the hierarchical tree of the studied coal concentrates, it is concluded that their distribution by classes in accordance with the brand identity and properties reflected in the results of technical, petrographic, elemental analysis and evaluation of structural indicators. It is shown that the elements of the sample form four natural clusters. On the basis of the obtained results, models have been developed to predict the yield of chemical coking products according to the characteristics of the quality of the initial coals.

Key words: coal coal quality coking chemical products cluster analysis

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