Uncertainty Analysis of Production in Open Pit Mines – operational parameter regression analysis of Mining Machinery
1
Luleå University of Technology, Luleå, Sweden
2
Department of Mining Engineering, Hamedan University of Technology, Hamedan, Iran
Autor do korespondencji
Amol A. Lanke
* Luleå University of Technology, Luleå, Sweden, Luleå University of Technology, 97787 Luleå, Sweden
* Luleå University of Technology, Luleå, Sweden, Luleå University of Technology, 97787 Luleå, Sweden
Mining Science 2016;23:147-160
SŁOWA KLUCZOWE
DZIEDZINY
STRESZCZENIE
In mining uncertainties related to equipment and operation are major reasons for loss of production. In order to address this issue a wide literature review was done in this study. It showed that reliability of equipment, spare part availability, automation of equipment are researched areas focused. However, a methodology which relates operational issues directly to production levels have been not studied with detailed analysis. In order to overcome this issue and propose, a method to achieve production assurance is the objective of this study. A case study with 2.5 years of data from a large open pit mine is carried out. Following the statistical principles, multiple regressions modeling with details analysis, optimization of payload and interpretation of analysis are used. It showed that at system level availability, utilization and maximum capacities are important criteria for finding root cause in loss of production. Model for shovel fleet showed that availability is most important characteristics hindering it to achieve higher level of production. It was also seen that 3 to 4 number of shovels are optimal for achieving current level of production. For truck fleet model represented that capacities involved are less important factor as compared to utilization of fleet.
REFERENCJE (23)
1.
ABDEL SABOUR S., DIMITRAKOPOULOS R., KUMRAL M., 2008. Mine design selection under uncertainty, Mining Technology 117,2, 53–64.
2.
ALARIE S., GAMACHE M., 2002. Overview of solution strategies used in truck dispatching systems for open pit mines, International Journal of Surface Mining, Reclamation and Environment 16,1, 59–76.
3.
BARABADI A. (2011), Production performance analysis: Reliability, maintainability and operational conditions. University of Stavanger,Norway, Phd. Thesis .
4.
BARABADI A., BARABADY, J. MARKESET T., 2011. A methodology for through- put capacity anal-ysis of a production facility considering environment condition, Reliability Engineering & System Safety 96,12, 1637–1646.
5.
DEHGHANI H., ATAEE-POUR M., 2012. Determination of the effect of operating cost uncertainty on mining project evaluation, Resources Policy 37,1, 109–117.
6.
DHILLON B. S. (2008), Mining equipment reliability, maintainability safety, Springer Science & Business Media.
7.
DIMITRAKOPOULOS R.G., SABOUR S.A.A., 2007. Evaluating mine plans un- der uncertainty: Can the real options make a difference? Resources Policy 32,3, 116–125.
8.
EKIPMAN A., BİR S.İ., SİSTEMİ K.D., PROSESİ A.H., 2003. A decision support system for optimal equipment selection in open pit mining: analytical hierarchy process. Istanbul University, Mining Engineering Department, 16(2), 1-11.
9.
ERCELEBI S., BASCETIN A., 2009. Optimization of shovel-truck system for surface mining, Journal of The Southern African Institute of Mining and Metallurgy 109,7, 433–439.
10.
GHODRATI B., 2005. Reliability and operating environment based spare parts planning, Lulea, Univer-sity of Technology, Ph.D Thesis.
11.
GUSTAFSON A., 2011. Automation of load haul dump machines, Lulea University of Technology, Ph.D.Thesis.
12.
HAIDAR A., NAOUM S., HOWES R., TAH J., 1999. Genetic algorithms application and testing for equipment selection, Journal of Construction Engineering and Management 125,1, 32–38.
13.
KAZAKIDIS V.N. (2001), Operating risk: Planning for flexible mining systems, University of British Columbia, PhD thesis.
14.
LANKE A.A., HOSEINIE S.H., GHODRATI B., 2016. Mine production index (MPI)-extension of OEE for bottleneck detection in mining. International Journal of Mining Science and Technology, 26,5, 753-760.
15.
MOHAMMADI M., RAI P., ORAEE K. KUMAR M., 2013. Analysis of availability and utilization of dragline for enhancement of productivity in surface mines-a case study, in: Proceedings of the 23rd World Mining Congress, Montreal.
16.
MOHAMMADI M., RAI P., SINGH U., SINGH S., 2016. Investigation of cycle time segments of drag-line operation in surface coal mine: A statistical approach, Geotechnical and Geological Engineering. 1–10.
17.
PATNAYAK S., TANNANT D., PARSONS I., DEL VALLE V., WONG J., 2008. Operator and dipper tooth influence on electric shovel performance during oil sands mining, International Journal of Min-ing, Reclamation and Environment 22,2, 120– 145.
18.
RAI P., TRIVEDI R., NATH R., 2000. Cycle time and idle time analysis of draglines for increased productivity-a case study, Indian Journal of Engineering and Materials Sciences 7,2, 77–81.
19.
RAMAZAN S., DIMITRAKOPOULOS R., 2013. Production scheduling with uncertain supply: a new solution to the open pit mining problem, Optimization and Engineering 14,2, 361–380.
20.
SAMANTA B., SARKAR B., MUKHERJEE S., 2002. Selection of opencast mining equipment by a multi-criteria decision-making process, Mining Technology 111,2, 136–142.
21.
SAMANTA B., SARKAR B., MUKHERJEE S., 2004. Reliability modelling and performance analyses of an LHD system in mining, Journal of the South African Institute of Mining and Metallurgy 104,1, 1–8.
22.
VERNON R., 1984. Uncertainty in the resource industries: the special role of state- owned enterprises, in: Risk and the Political Economy of Resource Development, Springer, 207–223.
23.
WALKER W.E., HARREMOËS P., ROTMANS J., VAN DER SLUIJS J. P., VAN ASSELT M. B., JANSSEN, P., KRAYER VON KRAUSS M. P., 2003. Defining uncertainty: a conceptual basis for uncertainty management in model-based decision support. Integrated assessment, 4(1), 5-17.
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| eISSN: | 2353-5423 |
| ISSN: | 2300-9586 |
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