Predicting the coke yield on the basis of the yield of coking products

Васильева, Е. В.; Cherkasova, T. G.; Kolmakov, N. G.; Subbotin, S. P.; Koshelev, E. A.; Nevedrov, A. V.; Papin, A. V.

doi:10.3103/s1068364x15110071

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Neural networks are a universal tool for solving the forecasting problem, however, at present, there are no universal tools developed using them. Based on the obtained quality indicators of the studied fuel samples and the by-products yield [11], as well as the analysis of the data of the studies using the statistical methods given in [12], which showed the nonlinearity of the relationship between the input and forecast parameters, a decision was made to develop and implement a Ward's network designing its own neural network architecture. The topology of the standard Ward's network used in studies consists in splitting neurons into groups with different transfer functions, which allows finding more complex nonlinear dependencies [13].…”

Section: Resultsmentioning

confidence: 99%

Development and Performance Evaluation of a Computer Program Based on Neural Network Mathematical Models for Forecasting By- Product Yield

2020

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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 [1]. 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.

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Section: Resultsmentioning

confidence: 99%

Development and Performance Evaluation of a Computer Program Based on Neural Network Mathematical Models for Forecasting By- Product Yield

2020

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“…Currently, the issues of improving coke quality are of particular relevance due to lower specific coke consumption for iron smelting, increased volume of blast furnaces and the introduction of technological measures to intensify the blast furnace performance. The importance of these problems is further compounded by the deterioration of the coking raw material base due to the instability in the grade composition of the supplied coal and coal concentrates, and the inconsistence of their quality parameters [1] [2]. The negative effect of coal concentrate grade composition deterioration can be compensated by an increase in the level of coal blending quality management which is primarily based on the chemical and technological properties of raw materials [3] [4].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Coking Capacity Indicator of Coking Coal Concentrates Based on the Research of Non-volatile Residue Strength Via Determination of the Coking Chemical Products Yield

Васильева¹,

Cherkasova²,

Subbotin³

et al. 2016

Proceedings of the 8th Russian-Chinese Symposium "Coal in the 21st Century: Mining, Processing, Safety"

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In modern conditions, one of the most acute problems for the coking industry include deterioration of raw material base for coking and coking coal deficit. The negative impact of the deterioration of the raw material condition on the yield and quality of coking products can be compensated by the increase in the level of coal blending quality management, which is primarily based on the chemical and technological properties of coal. Therefore, for the analysis of the above parameters, it is necessary to make a full use of the potential of all available coal properties valuation techniques. In the course of this work, the quality indicators of coal concentrates of the Kuznetsk Basin were defined and the strength properties of non-volatile residue obtained in determining the yield of chemical products of coking were studied. Based on the analysis of the obtained results, the experimental dependences of the reviewed parameters were built. Keywordscoal, volatile matters, chemical products of coking, non-volatile residue, non-volatile residue strength.

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