Predicting Wear and Temperature of Autonomous Haulage Truck Tires

Driverless haulage trucks have recently been developed for open pit mines. To predict the benefits of an Autonomous Haulage Truck (AHT) system, a deterministic/stochastic model has been created to compare an AHT fleet with a manual system by estimaing benchmarked Key Performance Indicators (KPIs) such as productivity, safety, breakdown frequencies, maintenance and labor costs, fuel consumption, tire wear, and cycle times. The goal of this paper is to describe the sub-model developed to predict tire wear of an AHT that functions within a virtual 24/7 open pit mine operating with 9 trucks and 2 shovels to move ore to a crusher and waste rock to a dump.