Volume 30, Issue 2, May 2017, Pages 234–241
Patrick Mukonki May1 and Augustin Muhota Kawinda2
1 Mining Engineering Department, University of Lubumbashi, Faculty of Engineering, University of Lubumbashi, Lubumbashi, Haut-Katanga province, RD Congo
2 Mining Engineering Department, University of Lubumbashi, Faculty of Engineering, University of Lubumbashi, Lubumbashi, Haut-Katanga province, RD Congo
Original language: English
Copyright © 2017 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Mashamba open pit mine is located 10 km southeast of Kolwezi town, in one of the richest copper areas of the Democratic Republic of Congo. The copper deposit block model has been run through mine optimization and provided a life of mine (LOM) of 6 years within four major pushbacks. The optimization process involved the use of NPV scheduler Datamine software (Lersch-Grossman algorithm). The ultimate pit and pushback designs have been executed using Minesight software. The haulage analysis has been conducted for the entire LOM of the deposit using the Minesight Haulage tool. Initial simulation of truck fleet size was done assuming a truck availability of 85%. However, it has since been observed that in practice, 85% availability over the entire life of the mine will be an unrealistic target it was therefore interesting to conduct a set of scenarios using the Minesight haulage tool to vary the availability and to compare the fleet size variation. In this paper, curve-fitting techniques have been used to observe, analyse, and establish a mathematical relationship to correlate dump truck availability and fleet size data, and to determine how strongly the two variables are correlated. Despite the fact that the mathematical equation and correlation factor calculated in this case study are not expected to lead to a replacement of haulage software packages, they were found to be helpful tools for quickly predicting the fleet size (number of trucks required for the entire mine life) based on a given set of truck availabilities at Mashamba open pit.
Author Keywords: Ordinary linear regression, analysis of variance, haulage, open pit mine, correlation.
Patrick Mukonki May1 and Augustin Muhota Kawinda2
1 Mining Engineering Department, University of Lubumbashi, Faculty of Engineering, University of Lubumbashi, Lubumbashi, Haut-Katanga province, RD Congo
2 Mining Engineering Department, University of Lubumbashi, Faculty of Engineering, University of Lubumbashi, Lubumbashi, Haut-Katanga province, RD Congo
Original language: English
Copyright © 2017 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Mashamba open pit mine is located 10 km southeast of Kolwezi town, in one of the richest copper areas of the Democratic Republic of Congo. The copper deposit block model has been run through mine optimization and provided a life of mine (LOM) of 6 years within four major pushbacks. The optimization process involved the use of NPV scheduler Datamine software (Lersch-Grossman algorithm). The ultimate pit and pushback designs have been executed using Minesight software. The haulage analysis has been conducted for the entire LOM of the deposit using the Minesight Haulage tool. Initial simulation of truck fleet size was done assuming a truck availability of 85%. However, it has since been observed that in practice, 85% availability over the entire life of the mine will be an unrealistic target it was therefore interesting to conduct a set of scenarios using the Minesight haulage tool to vary the availability and to compare the fleet size variation. In this paper, curve-fitting techniques have been used to observe, analyse, and establish a mathematical relationship to correlate dump truck availability and fleet size data, and to determine how strongly the two variables are correlated. Despite the fact that the mathematical equation and correlation factor calculated in this case study are not expected to lead to a replacement of haulage software packages, they were found to be helpful tools for quickly predicting the fleet size (number of trucks required for the entire mine life) based on a given set of truck availabilities at Mashamba open pit.
Author Keywords: Ordinary linear regression, analysis of variance, haulage, open pit mine, correlation.
How to Cite this Article
Patrick Mukonki May and Augustin Muhota Kawinda, “Establishment of a mathematical relationship to correlate fleet size with equipment availability in open pit mine, using curve-fitting techniques: A case study of Mashamba copper deposit in the Democratic Republic of Congo),” International Journal of Innovation and Scientific Research, vol. 30, no. 2, pp. 234–241, May 2017.
