The 7th Brazilian Conference on Intelligent Systems (BRACIS)

This paper develops a comprehensive artificial intelligence model, based on advanced data analytics methods, to improve trucks energy efficiency for surface mines. Payload, truck speed and the haul road total resistance are critical parameters that affect truck energy efficiency. The relationship between the principal parameters and the truck energy consumption is estimated by using an Artificial Neural Network (ANN) model. The ANN is trained, validated and tested using operational data collected from four large surface mines located in the United States of America and Australia. The ANN model efficiently creates a fitness function for the truck energy consumption. This function is applied to develop a digital learning algorithm based on a Genetic Algorithm (GA) and estimates the optimum values of effective haulage parameters to reduce the diesel fuel consumption by haul trucks at surface mines.

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