ENG  Vol.9 No.7 , July 2017
Analysis of Waste-Rock Transportation Process Performance in an Open-Pit Mine Based on Statistical Analysis of Cycle Times Data
Abstract: In this paper, the performance of a waste rock transportation process in an open pit mine was assessed by using cycle time data. A computerized truck-excavator dispatch system was used to record the cycle times. The process was broken into seven steps (or components of the total cycle), durations of which were recorded for a period of 1 month, leading to N = 60,690 data points or dispatches. The open pit mine studied consisted of 12 waste types loaded by 14 excavators and hauled by 49 trucks (at a trucks-to-excavator ratio of 3.5:1) in 75 changing locations. The string-type data was coded using integers to allow a FORTRAN code to extract process performance parameters using statistical analysis. The study established a wide range of parameters including: the waste material generation rate (about 1.73 million t/month, 81% comprising waste rock), truck fill factor, f, total cycle time (Tct), production capacity, theoretical cycle time, non-productive cycle time Tnp, and cycle time performance ratio (CTPR), denoted as Tpr. The factors affecting the process performance include: truck model, excavator model, location (haul distance and road conditions) and material type. For a fixed material type and tonnage, the PDFs of the cycle time components were logarithmic in nature, capable of differentiating performance variations under different factors. It was concluded that the performance of the waste material transportation system in this mine was determined to be acceptable due to mean value of Tpr = 2.432 being closer to unity. Reduction measures were suggested to minimize the cycle time for the process bottlenecks determined from Pareto analysis (that is, full haul, empty haul and loading processes).
Cite this paper: Manyele, S. (2017) Analysis of Waste-Rock Transportation Process Performance in an Open-Pit Mine Based on Statistical Analysis of Cycle Times Data. Engineering, 9, 649-679. doi: 10.4236/eng.2017.97040.

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