UBC Theses and Dissertations
An interactive simulation model to compare an autonomous haulage truck system with a manually-operated system Parreira, Juliana
This thesis presents a deterministic/stochastic model that was created to compare an Autonomous Haulage System (AHS) to a manual system by calculating and estimating benchmarked Key Performance Indicators (KPIs) such as productivity, safety, breakdown frequencies, maintenance costs, labour costs, fuel consumption, tire wear, and haulage cycle times. The manual system was verified against data provided by a major international mining company which cannot be identified for confidentiality reasons over a period of operation from Feb. 12 to Feb. 15, 2010. The mine that contributed the necessary data cannot be identified. For purposes of discussion, the mine is referred to as the Lucy mine in this thesis. Only a portion of the Lucy mine haulage system is modeled in this work with two shovels digging ore and waste to achieve a stripping ratio of 0.5. The results show that an autonomous haulage system is able to increase either production or productivity by 21.3% due to increased utilization. The autonomous mode shows an improvement in fuel consumption of 5.3% for L/cycle and 6.1% for L/t. Tire wear (mm/cycle) also shows an improvement of 7.6%. Although AHS trucks drive slower than normal drivers, the cycle time is shorter than manual because manual breaks involve assembling at the parking lot for safety purposes. A decrease in queuing time also occurred because of increased driving consistency. The AHS fleet queuing time decreased by 28.7% compared to the manual system. An economic assessment of an AHS system versus a manual fleet shows an after-tax incremental discounted cash flow rate of return of 48.7% in comparing a 7- truck AHS fleet with a 9-truck manual system both of which were designed to achieve equivalent production.
Item Citations and Data
Attribution-NonCommercial-NoDerivatives 4.0 International