Evolutionary optimisation of coal production in underground mines

• Autorzy: Brzychczy E. , Napieraj A. , Sukiennik M.

Warianty tytułu

PL

Optymalizacja produkcji w kopalniach węgla kamiennego z wykorzystaniem algorytmów ewolucyjnych

• Języki publikacji: EN

Abstrakty

EN

In the paper optimisation of coal production in multi-plant company is described. Optimisation problem and proposal of optimisation criterion were formulated. As modern solution in this area the developed evolutionary algorithm is presented. An example of calculation results is presented.

PL

W artykule przedstawiono zagadnienie optymalizacji produkcji w wielozakładowym przedsiębiorstwie górniczym. Zaprezentowano problem badawczy oraz kryterium optymalizacji. Jako nowe rozwiązanie w tym zakresie przedstawiono opracowany algorytm ewolucyjny. Zamieszczono również wyniki jego działania dla przykładowych danych.

Słowa kluczowe

EN

coal production; optimisation; evolutionary algorithm; OPTiCoalMine

PL

produkcja węgla kamiennego; optymalizacja; algorytm ewolucyjny; OPTiCoalMine

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2017
• Tom: z. 100
• Strony: 61--76
• Opis fizyczny: Bibliogr. 43 poz.

Twórcy

autor

Brzychczy E.

• AGH w Krakowie. Wydział Górnictwa i Geoinżynierii

autor

Napieraj A.

• AGH w Krakowie. Wydział Górnictwa i Geoinżynierii

autor

Sukiennik M.

• AGH w Krakowie. Wydział Górnictwa i Geoinżynierii

Bibliografia

• 1. Ataei M., Osandloo M.: Using a combination of genetic algorithm and the grid search method to determine optimum cutoff grades of multiple metal deposits. “International Journal of Surface Mining, Reclamation and Environment”, Vol. 18, No. 1, 2003, p. 60-78.
• 2. Barbaro R., Ramani R.: Generalized multiperiod MIP model for production scheduling and processing facilities selection and location. “Mining Engineering”. No. 38(2), 1986, p. 107-114.
• 3. Brzychczy E.: Metoda modelowania i optymalizacji robót górniczych w kopalni węgla kamiennego z wykorzystaniem sieci stochastycznych. Praca doktorska. Kraków 2005.
• 4. Brzychczy E.: A new solution supporting the designing process of mining operations in underground coal mines. Mine Planning and Equipment Selection: proceedings of the 22nd MPES conference, Vol. 1. Drebenstedt C., Singhal R. (eds.). Dresden, Germany, 14-19th October 2013. Springer, 2014.
• 5. Brzychczy E.: A method for modelling and optimisation of exploitation works in a multiplant mining enterprise. Rozprawy i Monografie, nr 245. AGH, Kraków 2012 (in Polish).
• 6. Brzychczy E.: A modern tool for modelling and optimisation of production in underground coal mine. In: eScience on distributed computing infrastructure: achievements of PLGrid Plus domain-specific services and tools. Bubak M., Kitowski J., Wiatr K. (eds.). Springer International Publishing, 2014.
• 7. Brzychczy E.: The planning optimisation system for underground hard coal mines. “Archives of Mining Sciences”, No. 56(2), 2011, p. 161-178.
• 8. Carlyle W.M., Eaves B.C.: Underground planning at Stillwater Mining Company. “Interfaces”, No. 31(4), 2001, p. 50-60.
• 9. Chanda E.K.C.: An application of integer programming and simulation to production planning for a stratiform ore body. “Mining Sci. Tech.”, No. 11(2), 1990, p. 165-172.
• 10. Ciepiela E., Nowakowski P., Kocot J., Haręźlak D., Gubała T., Mainzer J., Kasztelnik M., Bartyński T., Malawski M., Bubak M.: Managing Entire Lifecycles of e-Science Applications in GridSpace2 Virtual Laboratory – From Motivation through Idea to Operable Web-Accessible Environment Built on Top of PL-Grid e-Infrastructure, [in:] Bubak M., Szepieniec T., Wiatr K. (eds.): Building a National Distributed e-Infrastructure – PL-Grid – Scientific and Technical Achievements. “Lecture Notes in Computer Science”, Vol. 7136, 2012, p. 228-239.
• 11. Ciepiela E., Zaraska L., Sulka G.D.: GridSpace2 Virtual Laboratory Case Study: Implementation of Algorithms for Quantitative Analysis of Grain Morphology in Self-assembled Hexagonal Lattices According to the Hillebrand Method, [in:] Bubak M., Szepieniec T., Wiatr K. (eds.): Building a National Distributed e-Infrastructure – PL-Grid – Scientific and Technical Achievements. “Lecture Notes in Computer Science”, Vol. 7136, 2012, p. 240-251.
• 12. Denby B., Schofield D.: Open-pit design and scheduling by use of genetic algorithms. “Transactions of the Institution of Mining and Metallurgy, Section A: Mining Industry”, Vol. 103, 1994, p. 21-A26.
• 13. Denby B., Schofield D.: The use of genetic algorithms in underground mine scheduling. Proceedings of the 25th Symposium on the Application of Computers and Operations Research in the Mineral Industry. Brisbane, Australia 1995, p. 389-394.
• 14. Dornetto L.D.: An adaptive control scheme – expert system – that optimizes the operation of a proposed underground coal mining system with applications to shortwall, longwall and room pillar mining systems. Proc. IEEE Internat. Conf. Systems Man, Cybernetics. International Academic Publishers, Pergamon Press, Beijing 1988, p. 209-214.
• 15. Eiben A.E., Van Hemert J.I, Marchiori E., Steenbeek A.G.: Solving Binary Constraint Satisfaction Problems using Evolutionary Algorithms with an Adaptive Fitness Function. V PPSN, LNCS 1498, 1998, p. 196-205.
• 16. Epstein R., Gaete S., Caro F., Weintraub A., Santibanez P., Catalan J.: Optimizing long term planning for underground copper mines. Proc. Copper 2003-Cobre 2003, 5th Internat. Conf., Vol. I. Santiago, Chile, CIM and the Chilean Institute of Mining, 2003, p. 265-279.
• 17. Fava L., Millar D., Maybee B.: Scenario evaluation through mine schedule optimisation, [in:] Kuyvenhoven R., Rubio E., Smith M. (eds.). Proceedings of the 2nd International Seminar on Mine Planning. Gecamin, Santiago, Chile 2011, p. 1-10.
• 18. Gamache M., Grimard R., Cohen P.: A shortest-path algorithm for solving the fleet management problem in underground mines. “Eur. J. Oper. Res.”, No. 166(2), 2005, p. 497-506.
• 19. Grieco N., Dimitrakopoulos R.: Managing grade risk in stope design optimisation: Probabilistic mathematical programming model and application in sublevel stoping. “Mining Techology: IMM Trans. Sect.”, No. A116(2), 2007, p. 49-57.
• 20. Gunn E.A., Cunningham B., Forrester D.: Dynamic programming for mine capacity planning. Proceedings of the 23nd APCOM Symposium, Vol. 1. Montreal 1993, p. 529-536.
• 21. Huang Y., Kumar U.: Optimizing the number of load-hauldump machines in a Swedish mine by using queuing theory – A case study. “Internat. J. Surface Mining Reclamation Environ.”, No. 8(4), 1994, p. 171-174.
• 22. Jawed M.: Optimal production planning in underground coal mines through goal programming: A case study from an Indian mine. Elbrond J., Tang X. (eds.). Proc. 24th Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., CIM, Montréal 1993, p. 44–50.
• 23. Karbownik A., Poloczek F., Chroszcz H.: Podstawy projektowania kopalń. Część II. Politechnika Śląska, Gliwice 1991.
• 24. Kumral M.: Reliability-based optimisation of a mine production system using genetic algorithms. “J. Loss Prevention Process Indust.”, No. 18(3), 2005, p. 186-189.
• 25. Lemelin B., Abdel Sabour S.A., Poulin R.: An integrated evaluation system for mine planning under uncertainty. E. Magri, ed. Proc. 33rd Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., Santiago 2007, p. 262-269.
• 26. Magda R.: Modelowanie i optymalizacja elementów kopalń. Biblioteka Szkoły Eksploatacji Podziemnej, Seria z Lampką, Kraków 1999.
• 27. Magda R.: Mathematical model for estimating the economic effectiveness of production process in coal panels and an example of its practical application. “Internat. J. Prod. Econom.”, No. 34(1), 1994, p. 47-55.
• 28. Napieraj A., Snopkowski R.: Method of the production cycle duration time modeling within hard coal longwall faces. “Archives of Mining Sciences”, Vol. 57, No. 1, 2012, p. 121-138.
• 29. Newman A., Kuchta M.: Using aggregation to optimize long-term production planning at an underground mine. “Eur. J. Oper. Res.”, No. 176(2), 2007, p. 1205-1218.
• 30. Pędziwiatr T.: OPTiCoalMine calculation service: Source code, ACC Cyfronet, Cracow 2014.
• 31. Pendharkar P.C., Rodger J.A.: Nonlinear programming and genetic search application for production scheduling in coal mines. “Ann. Oper. Res.”, No. 95(1-4), 2000, p. 251-267.
• 32. Rahal D., Smith M., Van Hout G., Von Johannides A.: The use of mixed integer linear programming for long-term scheduling in block caving mines. Camisani-Calzolari F. (ed.). Proc 31st Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., SAIMM, Cape Town 2003, p. 123-131.
• 33. Samanta B., Bhattcherjee A., Ganguli R.: A genetic algorithms approach for grade control planning in a bauxite deposit. Proceedings of the 32nd International Symposium on Applications of Computers and Operations Research in the Mineral Industry. SME, Littleton, CO 2005, p. 337-342.
• 34. Sarin S.C., West-Hansen J.: The long-term mine production scheduling problem. “IIE Trans.”, No. 37(2), 2005, p. 109-121.
• 35. Simsir F., Ozfirat M.K.: Determination of the most effective longwall equipment combination in longwall top coal caving (LTCC) method by simulation modeling. “Internat. J. Rock Mech. Mining Sci.”, No. 45(6), 2008, p. 1015-1023.
• 36. Smith M., Sheppard I., Karunatillake G.: Using MIP for strategic life-of-mine planning of the lead/zinc stream at Mount Isa Mines. Camisani-Calzolari F. (ed.). Proc. 31st Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., SAIMM, Cape Town 2003, p. 465-474.
• 37. Snopkowski R.: Longwall output plan considered in probability aspect. “Arch. Min. Sci.”, No. 47(3), 2002, p. 413-420.
• 38. Song Z., Rinne M., van Wageningen A.: A review of real-time optimisation in underground mining production. “Journal of the Southern African Institute of Mining and Metallurgy”, Vol. 113, 2013, p. 889-897.
• 39. Sukiennik M., Snopkowski R.: Selection of the longwall face crew with respect to stochastic character of the production process. Pt. 1, Procedural description. “Archives of Mining Sciences”, Vol. 57, No. 4, 2012, p. 1071-1088.
• 40. Trout L.: Underground mine production scheduling using mixed integer programming. Proc. 25th Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sympos., AusIMM, 1995, p. 395-400.
• 41. Winkler B.: System for quality oriented mine production planning with MOLP. Proc. 27th Internat. Appl. Comput. Oper. Res. Mineral Indust. (APCOM) Sym-pos., Royal School of Mines, London 1998, p. 53-59.
• 42. Yun Q.X., Guo W.W., Che Y., Lu C.W., Lian M.I.: Evolutionary algorithms for the optimisation of production planning in underground mines. In Application of Computers and operations Researches in the Minerals Industries. South Africa Institute of Minig and Metallurgy, 2003.
• 43. Zhang M.: Combining genetic algorithms and topological sort to optimize open-pit mine plans. Cardu M., Ciccu R., Lovera E., Michelotti E. (eds.). 15th mine planning and equipment selection. FIORDO S.r.l., Torino 2006, p. 1234-1239.

Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).