The visualized results also clarify the general minimum areas in the plotted fuel consumption graphs. Finally, an artificial neural network is selected to predict haul truck fuel consumption. Third, the model is trained and tested using actual data from large surface mines in Australia, obtained through field research. This framework involves generating a fitness function from a model of the relationship between fuel consumption and its affecting factors. Second, developing a comprehensive analysis framework. Predicting truck fuel consumption can be accomplished by first identifying the significant factors affecting fuel consumption: total resistance, truck payload, and truck speed. This research seeks to develop an advanced data analytics model to estimate the energy efficiency of haul trucks used in surface mines, with the ultimate goal of lowering operating costs. Moreover, as the most costly aspect of surface mining with a significant environmental impact, diesel consumption will be investigated in this chapter. This chapter aims to provide an overview of energy efficiency in the mining industry with a particular focus on the role of fuel consumption in hauling operations in mining.
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