MLP-Based Model for Estimation of Methane Seam Pressure

One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring...

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Autores principales:	Marta Skiba, Barbara Dutka, Mariusz Młynarczuk
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gas seam pressure artificial neural networks (ANN) multilayer perceptron (MLP) geological hazard Technology T
Acceso en línea:	https://doaj.org/article/b0b6d474953a46699a7f8a824aa2d5ba
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spelling	oai:doaj.org-article:b0b6d474953a46699a7f8a824aa2d5ba2021-11-25T17:27:37ZMLP-Based Model for Estimation of Methane Seam Pressure10.3390/en142276611996-1073https://doaj.org/article/b0b6d474953a46699a7f8a824aa2d5ba2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7661https://doaj.org/toc/1996-1073One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the best fit were 2.59% and 3.04% for the average and maximum seam pressure values, respectively. The obtained determination coefficients (exceeding the value of 0.99) confirmed the very good predictive abilities of the models. These results imply a great potential for practical application of the proposed method.Marta SkibaBarbara DutkaMariusz MłynarczukMDPI AGarticlegas seam pressureartificial neural networks (ANN)multilayer perceptron (MLP)geological hazardTechnologyTENEnergies, Vol 14, Iss 7661, p 7661 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gas seam pressure artificial neural networks (ANN) multilayer perceptron (MLP) geological hazard Technology T
spellingShingle	gas seam pressure artificial neural networks (ANN) multilayer perceptron (MLP) geological hazard Technology T Marta Skiba Barbara Dutka Mariusz Młynarczuk MLP-Based Model for Estimation of Methane Seam Pressure
description	One of the principal indicators of the methane hazard in coal mines is gas pressure. This parameter directly affects the methane content in the seam as well as the rate of its release resulting from mining operations. Because of limitations in the existing methods for methane seam pressure measuring, primarily technical difficulties associated with direct measurement and the time-consuming nature of indirect measurement, this parameter is often disregarded in the coal and gas outburst forecasts. To overcome the above-mentioned difficulties, an attempt was made to estimate the methane seam pressure with the use of artificial neural networks. Two MLP-based models were developed to estimate the average and maximum methane seam pressure values, respectively. The analyses demonstrated high correlation between the values indicated by the neural models and the reference values determined on the basis of sorption isotherms. According to the adopted fit criterion, the prediction errors for the best fit were 2.59% and 3.04% for the average and maximum seam pressure values, respectively. The obtained determination coefficients (exceeding the value of 0.99) confirmed the very good predictive abilities of the models. These results imply a great potential for practical application of the proposed method.
format	article
author	Marta Skiba Barbara Dutka Mariusz Młynarczuk
author_facet	Marta Skiba Barbara Dutka Mariusz Młynarczuk
author_sort	Marta Skiba
title	MLP-Based Model for Estimation of Methane Seam Pressure
title_short	MLP-Based Model for Estimation of Methane Seam Pressure
title_full	MLP-Based Model for Estimation of Methane Seam Pressure
title_fullStr	MLP-Based Model for Estimation of Methane Seam Pressure
title_full_unstemmed	MLP-Based Model for Estimation of Methane Seam Pressure
title_sort	mlp-based model for estimation of methane seam pressure
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/b0b6d474953a46699a7f8a824aa2d5ba
work_keys_str_mv	AT martaskiba mlpbasedmodelforestimationofmethaneseampressure AT barbaradutka mlpbasedmodelforestimationofmethaneseampressure AT mariuszmłynarczuk mlpbasedmodelforestimationofmethaneseampressure
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MLP-Based Model for Estimation of Methane Seam Pressure

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