This paper describes a model that compares Autonomous Haulage Trucks (AHT) to a manual one by estimating benchmarked Key Performance Indicators (KPIs) such as productivity, safety, breakdown frequencies, maintenance and labour costs, fuel consumption, tire wear, and cycle times. The model uses a deterministic approach to model truck movement in an open pit and stochastic simulation to account for different events such as dumping, loading, queuing, breakdowns, downtime, and shift changes. The required data for vehicle motion include speed-rimpull characteristics of a mining truck together with the specifications of haul road profiles such as section length, maximum speed, maximum acceleration, and road resistance. Fuzzy logic has been used to model traction coefficients and rolling resistances in order to study the effect of these variables on the performance of an AHT compared to a manual one. The model extends conventional shovel/truck simulation into a variety of truck sub-systems such as controller systems, sensors, system communication and data collection, fuel consumption and tire wear to capture the mechanical complexities and physical interactions of these sub-systems with the mine environment on a 24/7 time basis. These sub-systems in the manual haulage case are constrained by driver performance which changes according to experience, personality, stress, and fatigue levels. Time in the shift and time during a work period also have major roles that affect performance. By studying different driver behaviours, the variances can be captured, studied, and used to validate the model. As a result, the “optimum driver†can be defined and set as input to model an AHT fleet. Running these two models in identical scenarios allow comparison of benchmarked KPIs, that can demonstrate the adaptability and utilization of an AHT.
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