| Cable shovel excavation in the Athabasca oil sands is vital given the in-situ excavation of formation. Random occurrence of boulders in the oil sands formation results in varying mechanical energy input and stress loading of the shovel handle-dipper tooth assembly. These problems pose significant failure threat to the shovel handle-dipper-teeth assembly resulting in unplanned downtimes, inefficiency and high production costs. A potential solution is the deployment of an intelligent shovel navigation technology. Currently, there are no prediction models, based on sound theory, for shovel navigation in this formation. This research is a pioneering effort toward developing dynamic models to accurately predict the formation resistances, optimal digging schemes and critical formation resistances on the dipper-teeth assembly.; Dynamic models of shovel digging, dipper material weight and the cutting resistance have been developed using the simultaneous constraint method, geometric simulation and the passive earth theory. These models have been combined into a cable shovel simulator. Factorial experimentation shows that the shovel performance is most sensitive to oil sand bulk density. A stochastic optimization scheme has been developed to optimize the oil sands digging. Experiments with the 2100BL and 4100TS shovels show that digging optimization is independent of shovel size. Over the defined domain, the optimal digging strategy is defined by crowd arm and hoist rope speeds of 0.25 ms-1 and 0.7 ms -1, respectively. The results show that the optimized digging conditions could reduce the digging time and energy per cycle by over 45% resulting in over 14% increase in annual production and over 35% decrease in energy costs. This is a pioneering study in oil sands excavation modeling providing insight into shovel excavation optimization. |
