The determination of haul trucks fuel consumption is complex and requires multiple parameters. Data analytics can be used to simulate the complex relationships between the input parameters affecting haul trucks fuel consumption. This chapter introduces an advanced data analytics model to improve the energy efficiency of haul trucks in surface mines.
The most important parameters affecting fuel consumption are first identified: payload, truck speed, and total rolling resistance. An analytical framework is then developed to determine the opportunities for minimizing the truck fuel consumption. The first stage of the analytical framework includes the development of artificial neural network model to determine the relationship between truck fuel consumption and payload, truck speed, and total resistance.
This model is trained and tested from some large surface mines in Australia, the United States, and Canada. A fitness function for haul truck fuel consumption is successfully generated. This is then used in the second stage of the analytical framework to develop a digital learning algorithm based on a novel multi-objective genetic algorithm. The aim of this algorithm is to establish the optimum arrangement of the three effective parameters to reduce the diesel fuel consumption, with these being specific to individual mines and fleet operations.