**Abstract**

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. Moreover, as the most costly aspect of surface mining with a significant environmental impact, diesel consumption will be investigated in this chapter. 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. Predicting truck fuel consumption can be accomplished by first identifying the significant factors affecting fuel consumption: total resistance, truck payload, and truck speed. Second, developing a comprehensive analysis framework. This framework involves generating a fitness function from a model of the relationship between fuel consumption and its affecting factors. Third, the model is trained and tested using actual data from large surface mines in Australia, obtained through field research. Finally, an artificial neural network is selected to predict haul truck fuel consumption. The visualized results also clarify the general minimum areas in the plotted fuel consumption graphs. These areas potentially open a new window for researchers to develop optimization models to minimize haul truck fuel consumption in surface mines.

**Keywords:** energy efficiency, fuel consumption, surface mining, artificial intelligence, prediction
