ORIGINAL PAPER
An Expert System for Equipment Selection of Thin Coal Seam Mining
Chen Wang 1,2
,
 
,
 
,
 
 
 
More details
Hide details
1
School of Mining, Guizhou University, Guiyang 550025, China
 
2
State Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, No. 1 University Road, Xuzhou, Jiangsu 221116, PR China
 
3
Mining College, Guizhou University
 
 
Submission date: 2019-02-22
 
 
Final revision date: 2019-06-25
 
 
Acceptance date: 2019-09-19
 
 
Publication date: 2019-09-19
 
 
Corresponding author
Chen Wang   

School of Mining, Guizhou University, Guiyang 550025, China
 
 
Gospodarka Surowcami Mineralnymi – Mineral Resources Management 2019;35(3):143-162
 
KEYWORDS
TOPICS
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.
METADATA IN OTHER LANGUAGES:
Polish
SPECJALISTYCZNY SYSTEM DOBORU SPRZĘTU DO WYDOBYCIA CIENKICH POKŁADÓW WĘGLA
system ekspercki, cienki pokład węgla, dobór i dopasowanie sprzętu, oprogramowanie do podejmowania decyzji, zastosowanie w praktyce
Dobór sprzętu, jako jedna z kluczowych technik w pełni zmechanizowanego procesu wydobycia, ma ogromny wpływ na bezpieczeństwo, produkcję i wydajność. Wybór i dopasowanie w pełni zmechanizowanego sprzętu górniczego w cienkim pokładzie węgla jest ograniczone przez wiele czynników. W przypadku całkowicie zmechanizowanej ściany wydobywczej węgla (FMM) w cienkich pokładach (TCS) przeciwdziałanie problemom związanym z wyborem sprzętu, takim jak m.in.: wielość rozpatrywanych parametrów i niska automatyzacja, ustanowiono system ekspercki (ES) doboru sprzętu do w pełni zmechanizowanej ściany wydobywczej. Utworzono bazę danych systemu doboru i dopasowania systemu eksperckiego w cienkich pokładach węgla w pełni zmechanizowanej ściany wydobywczej. Jednocześnie opracowano oprogramowanie do podejmowania decyzji, dopasowane do ES. Na podstawie kilku rzeczywistych przykładów omówiono wiarygodność i ryzyko techniczne związane z wynikami ES. W porównaniu z zastosowaniem obecnym, wybór kombajnu systemem eksperckim (ES) jest niezawodny. Wystąpiły jednak pewne niewielkie odchylenia w wyborze stojaków hydraulicznych i przenośnika zgarniającego, następnie ES został ulepszony. W rezultacie poprawiono wybór sprzętu w całkowicie zmechanizowanej ścianie wydobywczej o nazwie 4301 w kopalni Liangshuijing. Interfejs wyników wyboru sprzętu w ulepszonym ES jest zgodny z propozycją projektu 4301 FMM roboczej ściany wydobywczej. Niezawodność ulepszonego ES może spełniać wymagania inżynieryjne. Promuje inteligentne i wydajne wydobycie zasobów węgla w Chinach.
 
REFERENCES (31)
1.
Baker et al. 2004 – Baker, C.G.J., Lababidi, H.M.S., and Masters, K. 2004. A fuzzy expert system for the selection of spray-drying equipment. Drying Technology 22(1–2), pp. 237–258.
 
2.
Basçetin et al. 2005 – Basçetin, A., Öztas, O. and Kanli, A.I. 2005. EQS: a computer software using fuzzy logic for equipment selection in mining engineering. Applied Mathematics and Computation 1(161), pp. 707–720.
 
3.
Dağdeviren, M. 2008. Decision making in equipment selection: an integrated approach with AHP and PROMETHEE. Journal of Intelligent Manufacturing 19(4), pp. 397–406.
 
4.
Brzychczy et al. 2017 – Brzychczy, E., Kęsek, M., Napieraj, A. and Magda, R. 2017. An expert system for underground coal mining planning, Gospodarka Surowcami Mineralnymi – Mineral Resources Management 33(2), pp. 113–127.
 
5.
Fonseca et al. 2004 – Fonseca, D.J., Uppal, G., and Greene, T.J. 2004. A knowledge-based system for conveyor equipment selection. Expert Systems with Applications 26(4), pp. 615–623.
 
6.
Fu, Q. 2005. Research and development on coal mining method selection and expert system of completed equipment selection. Coal Science and Technology 33(7), pp. 64–68.
 
7.
Gao, Q.F. and Liu, X.H. 2012. Research on full-mechanized mining equipment matching management system for Shendong group. Coal Mining Technology 17(6), pp. 98–100.
 
8.
Han, C.S. 2013. Technology of fully mechanized thin seam mining in Jiangjiawan mine of Datong minging area. Coal Science and Technology 41(supp), pp. 11–12.
 
9.
He et al. 2012 – He, J., Dou, L. M. and Lu, C.P. 2012. Characteristic and prevention research on rock burst of thin coal seam. Journal of China Coal Society, 37(7), pp. 1094–1098.
 
10.
Jiang et al. 2009 – Jiang, J.Q., Qu, T.Z., Dai, J., Li, H. and Tian, Z.C. 2009. Numerical test of spalling of iron sulfide concretions in thin seam. Journal of China Coal Society 34(4), pp. 472–477.
 
11.
Liu et al. 1992 – Liu, F.M., Zhang, S.X. and Cao, S.W. 1992. An expert system to select the type of rolling bearings. Machine Design 6, pp. 8–11.
 
12.
Liu, J.R. 2011. Practices on fully mechanized coal mining equipment matched and mining in thin seam of Datong mining area. Coal Science and Technology 39(11), pp. 40–43.
 
13.
Ma, J. and Lu, S. 2011. Parametric matching system development of fully-mechanized face equipments based on UG. Coal Mine Machinery 32(5), pp. 211–213.
 
14.
Nguyen et al. 2007 – Nguyen, H.T., Dawal, S.Z.M., Nukman, Y. and Aoyama, H. 2014. A hybrid approach for fuzzy multi-attribute decision making in machine tool selection with consideration of the interactions of attributes. Expert Systems with Applications 41(6), pp. 3078–3090.
 
15.
Samvedi et al. 2012 – Samvedi, A., Jain, V., and Chan, F.T. 2012. An integrated approach for machine tool selection using fuzzy analytical hierarchy process and grey relational analysis. International Journal of Production Research 50(12), pp. 3211–3221.
 
16.
Sheng et al. 2007 – Sheng, G.J., Sun, Q.S. and Song, H.L. 2007. The innovational mining technology of fully mechanized mining on thin coal seam. Journal of China Coal Society 32(3), pp. 230–234.
 
17.
Taha, Z. and Rostam, S. 2011. A fuzzy AHP–ANN-based decision support system for machine tool selection in a flexible manufacturing cell. The International Journal of Advanced Manufacturing Technology 57(5–8), pp. 719–733.
 
18.
Wang, K. W. and Luo, Y. Q. 2013. Efficient mining techniques in thin coal seam with complex structure and extra- -long face. Coal Mine Modernization 3, pp. 17–19.
 
19.
Xiao et al. 1996 – Xiao, S.D., Tao, C.D., Du, C.L. and Cao, H.B. 1996. Design of the Software for the Fully-Mechanized Mining Equipment Selection. Journal of China University of Mining &Technology 25(1), pp. 62–65.
 
20.
Xu, G.X. 2012. The expert system database of equipment selection researched on mechanized mining face. Taiyuan University of Technology, pp. 42–43.
 
21.
Yi, W.J. and Li, H. 2005. Bayesian network based expert system for damage diagnosis of the durability of the reinforced concrete. Systems Engineering – Theory and Practice 9, pp. 105–111.
 
22.
Yilmaz, B., and Dağdeviren, M. 2011. A combined approach for equipment selection: F-PROMETHEE method and zero–one goal programming. Expert Systems with Applications 38(9), pp. 11641–11650.
 
23.
Yin et al. 2010 – Yin, L., Tong, H.L. and Zhu, Z.C. 2010. Computer aided design completed set equipment selection and layout of fully mechanized coal mining face. Coal Science and Technology 38(7), pp. 81–84.
 
24.
Yuan, L. 2011. Thin coal-seam mining technology and equipment research. Coal Mining Technology 16(3), pp. 15–18.
 
25.
Yuan et al. 2019 – Yuan, Y., Chen, Z., Yuan, C. et al. 2019. Numerical Simulation Analysis of the Permeability Enhancement and Pressure Relief of Auger Mining. Nat Resour Res.
 
26.
Zeng et al. 2009 – Zeng, Q.L., Fang, C.H., Liu, Z.H. and Hao, N.N. 2009. Expert system of matching selection of equipments of top-coal caving fully-mechanized face. Coal Mine Machinery 30(1), pp. 120–122.
 
27.
Zhai et al. 2009 – Zhai, X.X., Chen, D.H., Guo, H.B., Lu, J.M. and Li, X. J. 2009. Research on equipments coordination for shearer in soft coal thin seam with hard roof. Colliery Mechanical and Electrical Technology (3), pp. 7–10.
 
28.
Zhang, M.Q. and Liu, Y. 2002. Study of fuzzy expert system for equipment selection by risky decision-making. Journal of China Coal Society 27(6), pp. 649–652.
 
29.
Zhao et al. 2006 – Zhao, Y.L., Gao, Z.Q., Lu, L.P. and Huang, S.L. 2006. Gray theories and analytical method of mining and processing equipment chooses. Coal Mine Machinery 27(8), pp. 66–68.
 
30.
Zheng, W. 2010. Equipment selection and matching in thin seam mining. Coal Engineering 3, pp. 4–6.
 
31.
Zhu et al. 2001 – Zhu, C.Q., Miao, X.X. and Xiao, H.F. 2001. Application of neural network method in fully mechanized sub-level caving face. Journal of China Coal Society 26(3), pp. 249–252.
 
eISSN:2299-2324
ISSN:0860-0953
Journals System - logo
Scroll to top