Optimization Analysis of Power Coal-Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm
Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal-blending system has some defects, such as fuzzy ash content of coal-blending warehouse products and excessive error of th...
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| Vydáno v: | Journal of sensors Ročník 2023; číslo 1 |
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| Hlavní autoři: | , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Hindawi
27.02.2023
John Wiley & Sons, Inc |
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| ISSN: | 1687-725X, 1687-7268 |
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| Abstract | Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal-blending system has some defects, such as fuzzy ash content of coal-blending warehouse products and excessive error of the coal-blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal-blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal-blending research, then establishes an optimized dynamic coal-blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal-blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal-blending method, the average error is 3.33%, and the accuracy is improved by 4.82%. |
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| AbstractList | Research and optimization of the coal‐blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing research on the coal‐blending system has some defects, such as fuzzy ash content of coal‐blending warehouse products and excessive error of the coal‐blending ratio determined by artificial experience, which brings many disadvantages and difficulties to the efficient and stable production of the coal preparation plant. The genetic algorithm and intelligent sensor network are relatively advanced technologies at present. Therefore, the optimization analysis of the coal‐blending model and its control system based on the intelligent sensor network and genetic algorithm has become a research hotspot. This paper first introduces the basic theory of the genetic algorithm and intelligent sensor network and their application in coal‐blending research, then establishes an optimized dynamic coal‐blending model based on the intelligent sensor network and genetic algorithm, and analyzes its application effect. The research results show that the coal quality prediction model can be mined from coal quality data and coal‐blending data by using the genetic algorithm and ideas, and the monitoring system based on the intelligent sensor network can monitor the abnormal state anytime and anywhere. Compared with the traditional coal‐blending method, the average error is 3.33%, and the accuracy is improved by 4.82%. |
| Author | Song, Wenge Zhang, Sa Zhu, Ziqi Shang, Ming Tao, Yadong Lu, Chao Wang, Bin |
| Author_xml | – sequence: 1 givenname: Yadong surname: Tao fullname: Tao, Yadong organization: Shendong Coal Preparation CenterShenmuShaanxi 719315China – sequence: 2 givenname: Wenge surname: Song fullname: Song, Wenge organization: Shendong Technology Research InstituteShenmuShaanxi 719315China – sequence: 3 givenname: Ziqi surname: Zhu fullname: Zhu, Ziqi organization: Shendong Coal Preparation CenterShenmuShaanxi 719315China – sequence: 4 givenname: Bin surname: Wang fullname: Wang, Bin organization: Shendong Technology Research InstituteShenmuShaanxi 719315China – sequence: 5 givenname: Ming surname: Shang fullname: Shang, Ming organization: Shendong Coal Preparation CenterShenmuShaanxi 719315China – sequence: 6 givenname: Sa orcidid: 0000-0002-5462-7605 surname: Zhang fullname: Zhang, Sa organization: Bain Spec Technology (Beijing) Co.Ltd.ChaoyangBeijing 100102China – sequence: 7 givenname: Chao surname: Lu fullname: Lu, Chao organization: Bain Spec Technology (Beijing) Co.Ltd.ChaoyangBeijing 100102China |
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| Cites_doi | 10.3390/pr6110226 10.1016/j.applthermaleng.2017.06.112 10.1016/j.enconman.2020.113321 10.1016/j.conengprac.2020.104562 10.1016/j.fuel.2018.02.061 10.1186/s13638-020-01790-6 10.1109/CSIT49958.2020.9321953 10.1016/j.energy.2013.02.033 10.3390/en13112935 10.3390/en13246587 10.1109/TEC.2002.805182 10.1016/j.ipm.2021.102844 10.1016/j.energy.2013.02.062 10.1109/ICMLC.2010.5580941 10.1038/s41598-018-20404-2 10.3390/pr7070409 10.1016/j.comnet.2019.04.017 10.1016/j.ijggc.2018.12.004 |
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| Copyright | Copyright © 2023 Yadong Tao et al. Copyright © 2023 Yadong Tao 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 |
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| Snippet | Research and optimization of the coal-blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing... Research and optimization of the coal‐blending system can greatly improve the variety diversity and quality stability of finished coal. However, the existing... |
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| Title | Optimization Analysis of Power Coal-Blending Model and Its Control System Based on Intelligent Sensor Network and Genetic Algorithm |
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