**7. Conclusion**

As old industry mining uses traditional approaches to solve the business problem, energy efficiency improvement is one of the most critical challenges in the mining industry. Mine managers and researchers can benefit from digital transformation and data access by utilizing innovative data-driven solutions such as machine learning and artificial intelligence. This chapter presented a practical framework and developed an artificial neural network algorithm to predict the consumed fuel by haul trucks in surface mines. The successful results of deploying this application in different mines sites have opened a new window for researchers to use the sophisticated AI models to tackle the mining operation challenges. This chapter showed the prediction results for diesel consumption, and it is clear that the prediction is the starting point of advanced analytics. The developed model was used in a large surface mine in Australia and tested on two different trucks (CAT 793D and Komatsu HD785). For both tested trucks, the accuracy of developed mold was reported more than 85%, an acceptable result for a sophisticated AI model that unstructured and noisy datasets have fed. There are more opportunities to use AI to optimize and make decisions to increase energy efficiency in mining engineering.

*Energy Efficiency Improvement in Surface Mining DOI: http://dx.doi.org/10.5772/intechopen.104262*
