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Abstract

As one of the key techniques in the fully mechanized mining process, equipment selection and matching has a great effect on security, production and efficiency. The selection and matching of fully mechanized mining equipment in thin coal seam are restricted by many factors. In fully mechanized mining (FMM) faced in thin coal seams (TCS), to counter the problems existing in equipment selection, such as many the parameters concerned and low automation, an expert system (ES) of equipment selection for fully mechanized mining longwall face was established. A database for the equipment selection and matching expert system in thin coal seam, fully mechanized mining face has been established. Meanwhile, a decision-making software matching the ES was developed. Based on several real world examples, the reliability and technical risks of the results from the ES was discussed. Compared with the field applications, the shearer selection from the ES is reliable. However, some small deviations existed in the hydraulic support and scraper conveyor selection. Then, the ES was further improved. As a result, equipment selection in fully mechanized mining longwall face called 4301 in the Liangshuijing coal mine was carried out by the improved ES. Equipment selection results of the interface in the improved ES is consistent with the design proposal of the 4301 FMM working face. The reliability of the improved ES can meet the requirements of the engineering. It promotes the intelligent and efficient mining of coal resources in China.

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Authors and Affiliations

Chen Wang
ORCID: ORCID
Jie Chen
Cheng Liu
Chengyu Jiang
ORCID: ORCID
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Abstract

As one of the most important decision-making problems in fully mechanised mining, the corresponding mining technology pattern is the technical foundation of the working face. Characterised by complexity in a thin seam fully mechanised mining system, there are different kinds of patterns. In this paper, the classification strategy of the patterns in China is put forward. Moreover, the corresponding theoretical model using neural networks applied for patterns decision-making is designed. Based on the above, optimal selection of these patterns under given conditions is achieved. Lastly, the phased implementation plan for automatic mining pattern is designed. As a result of the industrial test, automatic mining for panel 22204 in Guoerzhuang Coal Mine is realised.
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Authors and Affiliations

Chen Wang
1 2
ORCID: ORCID
Yu Zhang
1
ORCID: ORCID
Yong Liu
1
ORCID: ORCID
Chengyu Jiang
1
ORCID: ORCID
Mingqing Zhang
1
ORCID: ORCID

  1. Guizhou University, Mining College, Guiyang 550025, China
  2. Chongqing Energy Investment Group Science & Technology co., LTD, Chongqing 400060, China

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