Shovel-truck cycle simulation methods in surface mining - Krause, Andre James (My University, 2008)
This study investigates the main factors of production, their interaction and influence on cycle time efficiency for shovel-truck systems on surface mines. The main factors are truck payload, cycle time and operator proficiency. It is now routine that shoveltruck cycles are analysed using simulation methods. The Elbrond, FPC, Talpac, Arena and Machine Repair simulation models are discussed to explain how their model characteristics contribute to the differences in their reported cycle efficiency as indicated by productivity results. The Machine Repair Model based on Markov chains is adapted for shovel-truck systems and examined for calculating shovel-truck cycle times. The various probability distributions that can be use to model particular cycle time variables and some methods in selecting the “best” fit are examined. Truck cycle time variable sensitivity is examined by using the Excel® add-on program @Risk (Palisade Corp.) in determining their respective weighting or contribution within the total cycle time variability. The analysis of cycle efficiency leads ultimately to sizing of a shovel-truck system. When determining a fleet size for a particular surface operation the planning engineers will tend to use one and to a lesser extent perhaps two separate simulation models. This study calculates the productivity (tonnes per hour) for a “virtual mine” with a variable number of trucks, variable cycle distances and variable truck loading times. The study also includes a separate analysis of cycle time variables and their probability distributions for the Orapa diamond mine in Botswana, to show possible distributions for various cycle variables. The study concludes with a calculation of the truck fleet size using the Elbrond, FPC, Talpac and Arena and Machine Repair models for the Optimum Colliery coal mine and then compares the results and their correlation. The main findings are that the calculation of waiting time is different for the various models, each model yields a unique fleet sizing solution and any solution in effect represents a range of results.
Keywords: probability distributions; queuing theory; bunching; maching; Monte Carlo simulation