Predicting the Yield of Coking Products

Васильева, Е. В.; Doroganov, V. S.; Piletskaya, A. B.; Cherkasova, T. G.; Pimonov, A. G.; Kolmakov, N. G.; Subbotin, S. P.; Nevedrov, A. V.; Papin, A. V.

doi:10.3103/s1068364x17090071

Cited by 5 publications

(4 citation statements)

References 4 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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“…One of the problems of using evolutionary computations to optimize multiextremal functions is to get into a local extremum. To solve this problem, it is possible to use hybrid genetic algorithms [11][12][13]. The idea of hybrid algorithms is to combine a genetic algorithm with some other classical search method suitable for solving this problem.…”

Section: Application Of Heuristic Methods Of Finding the Optimal Consmentioning

confidence: 99%

Optimization Methods and Algorithms for Calculating the Construction of Non-Rigid Pavement for Technological Quarries Roads

Tailakova

Pimonov

2019

E3S Web Conf.

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Previously developed by the authors of the optimization model for calculating the construction of non-rigid pavement of public roads is proposed to be used for the calculation of the construction of pavement technological career roads. The article describes the optimization methods and algorithms for calculating the construction of pavement. Possibilities of using methods of coordinate-wise descent, multi-start, dynamic programming for the selection of the optimal construction of pavement are presented. Application of genetic algorithms for the decision of an optimization problem of calculation of a construction of pavements on the basis of comparison of their efficiency with efficiency of search methods is proved. Described results of computational experiment of selection of genetic algorithm operators to reduce the volume of calculations and ensure the stability of the results.

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“…To design the cost-effective flexible road pavements, it is proposed to use a genetic algorithm. Genetic algorithm is based on an evolutionary search method that can be flexibly configured [9][10][11][12][13][14][15]. To carry out a computational experiment, 100 runs of each genetic algorithm modification were considered [1].…”

Section: Th International Innovative Mining Symposiummentioning

confidence: 99%

“…The advantage of genetic algorithms is the possibility to combine them with other optimization methods. A common approach is the inclusion of nonlinear programming methods into genetic algorithm for local optimization of chromosomes [1,[9][10][11][12][13][14][15].…”

Section: Th International Innovative Mining Symposiummentioning

confidence: 99%

Evolutionary computation and genetic algorithms for designing the optimal mine haul road pavements

Tailakova

Pimonov

Mahambayev³

2021

E3S Web Conf.

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An overview of the software for designing flexible road pavements used in the USA, European countries and the Russian Federation is given, conceptual and mathematical models for designing flexible road pavements with the minimum cost per a linear meter are presented, the application of evolutionary algorithms in combination with exhaustive search and parallel computing is substantiated, the developed hybrid genetic algorithm for the optimization of flexible road pavement designs in terms of value of materials of layers is described, the results of a simulation experiment performed using the software developed within the framework of the study are presented in the article. It is proposed to use the developed software and algorithms for designing the optimal haul road pavements in terms of material cost.

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Predicting the Yield of Coking Products

Cited by 5 publications

References 4 publications

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

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

Optimization Methods and Algorithms for Calculating the Construction of Non-Rigid Pavement for Technological Quarries Roads

Evolutionary computation and genetic algorithms for designing the optimal mine haul road pavements

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