Greenhouse Gas Emission Reduction for Mining Fleets via Smart Dispatching Based on Deep Reinforcement Learning


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In typical mining operations, more than half of the direct greenhouse gas (GHG) emissions come from haulage fuel consumption. Smarter truck fleet dispatching is a feasible and manageable solution to reduce direct emissions with existing equipment. Conventional scheduling-based and human-led dispatching solutions often cause lower efficiency that wastes resources and elevates emissions. In this study, a simulated environment is developed to enable testing smarter real-time dispatching systems, Q-learning as a model-free reinforcement learning algorithm is used to improve fleet productivity, decrease waiting time and, consequently, reduce GHG emissions. The proposed algorithm trains the fleet to make better decisions based on payload, traffic, queueing, and maintenance conditions. Results show that this solution can reduce GHG emissions from haulage fuel consumption by over 30% while achieving the same production levels as compared to fixed scheduling. The proposed solution also shows advantages in handling operational randomness and balancing fleet size, productivity, and emissions.