**7. Conclusion**

The purpose of this chapter was to demonstrate the value of modern data analytics models in improving energy efficiency in mining sectors, particularly in haulage operations, which are one of the most energy-intensive activities. However, improving haul truck fuel consumption for actual mining operations based on the link between influential factors, such as P, S, and T.R., was difficult. Thus, two AI methods were utilized to construct a reliable model to assess the problem.

At first, an ANN model was utilized to simulate truck fuel consumption as a function of payload, truck speed, and total resistance. Then, the ANN was generated and tested using the collected accurate mine site datasets, and the results showed good agreement between the actual and estimated values of FCIndex.

After that, to improve the energy efficiency in haulage operations, a GA method was developed to determine the optimal value of effective parameters on fuel consumption in haulage trucks. The developed model was used to analyze data for two surface mines in Australia and Iran. This model also can be applied to improve the haul truck fuel consumption for any dataset obtained from actual mine operations.

The results of two successful case studies show plenty of opportunities to use advanced analytics and AI in the mining industry to improve energy efficiency.

*Improve Energy Efficiency in Surface Mines Using Artificial Intelligence DOI: http://dx.doi.org/10.5772/intechopen.101493*
