Improve Energy Efficiency in Surface Mines Using Artificial Intelligence

This chapter demonstrates the practical application of artificial intelligence (AI) to improve energy efficiency in surface mines. The suggested AI approach has been applied in two different mine sites in Australia and Iran, and the achieved results have been promising. Mobile equipment in mine sites consumes a massive amount of energy, and the main part of this energy is provided by diesel. The critical diesel consumers in surface mines are haul trucks, the huge machines that move mine materials in the mine sites. There are many effective parameters on haul trucks’ fuel consumption. AI models can help mine managers to predict and minimize haul truck energy consumption and consequently reduce the greenhouse gas emission generated by these trucks. This chapter presents a practical and validated AI approach to optimize three key parameters, including truck speed and payload and the total haul road resistance to minimize haul truck fuel consumption in surface mines. The results of the developed AI model for two mine sites have been presented in this chapter. The model increased the energy efficiency of mostly used trucks in surface mining, Caterpillar 793D and Komatsu HD785. The results show the trucks’ fuel consumption reduction between 9 and 12%.

Long-Haul Vehicle Routing and Scheduling with Biomathematical Fatigue Constraints

Hours of service (HOS) regulations are among the conventional safety constraints that are compiled by long-haul truck drivers. These regulations have been considered in models and algorithms of vehicle routing problems to assign safe schedules to drivers. However, the HOS regulations neglect a few crucial fatigue risk factors and, at times, fail to generate fatigue-reducing schedules. In this study, a set of biomathematical fatigue constraints (BFCs) derived from biomathematical models are considered for a long-haul vehicle routing and scheduling problem. A BFC scheduling algorithm and a BFC-HOS scheduling algorithm have been developed and then embedded within a tabu search heuristic to solve the combined vehicle routing and scheduling problem. All the solution methods have been tested on modified Solomon instances and a real-life instance, and the computational results confirm the advantages of employing a sophisticated and fatigue-reducing scheduling procedure when planning long-haul transportation.