Optimization of Process Control Parameters for Fully Mechanized Mining Face Based on ANN and GA
In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to i...
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| Published in: | Computational intelligence and neuroscience Vol. 2021; no. 1; p. 5557831 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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New York
Hindawi
2021
John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face. |
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| AbstractList | In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face. In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face.In the traditional optimization mathod, the process control parameters for fully mechanized mining face are determined by experts or technicians based on their own experience, which is lack of scientific basis, and need long production adjustment cycle. It is cause large loss, and not conducive to improving mine production efficiency. In order to solve this problem, the study proposes a process control parameter optimization method based on a mixed strategy of artificial neural network and genetic algorithm and uses a cross-entropy cost function to optimize an artificial neural network, which improves the learning speed and fitting accuracy of the neural network. Using the historical production data of a fully mechanized coal mining face, taking the pulling speed of the shearer, hydraulic support moving speed, chain speed of scraper conveyor, chain speed of stage loader, emulsion pump outlet pressure, and spray pump outlet pressure as the optimization objects and taking the value range of each process control parameter as a constraint condition to establish a mixed strategy optimization model of process control parameters for a fully mechanized mining face, each process control parameter is optimized with the output of coal per minute as the optimization goal. The results show that the method has high accuracy and short optimization process time and can effectively improve the production efficiency of the working face. |
| Audience | Academic |
| Author | Xu, Zhihai Pan, Tao Li, Qi Zhao, Hongze |
| AuthorAffiliation | 1 School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing 100083, China 3 CHN Energy Information Technology Co., Ltd., Beijing 100011, China 2 State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China |
| AuthorAffiliation_xml | – name: 2 State Key Laboratory for Geomechanics and Deep Underground Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China – name: 1 School of Energy and Mining, China University of Mining and Technology (Beijing), Beijing 100083, China – name: 3 CHN Energy Information Technology Co., Ltd., Beijing 100011, China |
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| Cites_doi | 10.1177/1687814019850720 10.1109/TII.2019.2908989 10.1016/j.fuel.2020.118338 10.1016/j.jastp.2020.105328 10.1049/iet-smt.2016.0425 10.1016/j.ijheatmasstransfer.2020.12014 10.1016/j.ejor.2020.02.047 10.1061/(ASCE)HE.1943-5584.0001954 10.1016/j.chemosphere.2020.127081 10.1016/j.apacoust.2020.107490 10.1016/j.applthermaleng.2020.115794 10.1016/j.catena.2020.105114 10.13272/j.issn.1671-251x.2015.07.002 10.21278/TOF.451018520 10.1016/j.jhydrol.2020.125343 10.1016/j.measurement.2019.05.019 10.1016/j.foodchem.2020.127862 10.19769/j.zdhy.2019.09.032 10.1016/J.ASOC.2021.107318 10.1016/j.jclepro.2020.122189 10.3390/sym12040622 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Hongze Zhao et al. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Hongze Zhao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2021 Hongze Zhao et al. 2021 |
| Copyright_xml | – notice: Copyright © 2021 Hongze Zhao et al. – notice: COPYRIGHT 2021 John Wiley & Sons, Inc. – notice: Copyright © 2021 Hongze Zhao et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2021 Hongze Zhao et al. 2021 |
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| SubjectTerms | Algorithms Analysis Artificial neural networks Automation Chain conveyors Chains Coal mines Coal mining Conveying machinery Cooperation Cost function Efficiency Entropy (Information theory) Expected values Friction stir welding Genetic algorithms Hydraulics Industrial production Learning theory Mean square errors Mineral industry Mining industry Neural networks Optimization Process controls Process parameters Production capacity |
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| Title | Optimization of Process Control Parameters for Fully Mechanized Mining Face Based on ANN and GA |
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