A Q-learning approach to waste rock reduction in open-pit mine design based on cleaner production principles

Large-scale metal mining operations extract vast quantities of ore and waste rock annually, generating both economic benefits and significant environmental challenges. While mining supports industrial growth, technological advancement, and job creation, it also imposes substantial social and ecological costs, particularly from the disposal of waste rock. These mine wastes increase the burden of handling, dumping, reclamation, and long-term monitoring, often undermining sustainability objectives. Reducing waste generation within ultimate pit limit design is therefore essential to align mining with sustainable development policies. This study develops a novel framework that integrates mathematical modeling with Q-learning, a reinforcement learning algorithm, to optimize ultimate pit limit by maximizing ore recovery and profitability while minimizing waste rock extraction. A key innovation of the model is the explicit inclusion of environmental costs, covering prevention, mitigation, and compensation of impacts, as a fundamental component of block economic value. The approach is validated using a large scale copper ore deposit, and compared against the widely used Lerchs-Grossmann algorithm. Results show that the Q-learning framework reduces waste rock extraction by 2.7 million tons, with about 0.5 million tons less ore recovered, while also lowering computational time from 7.2 to 5.8 h. Although Lerchs-Grossmann yields slightly higher profit, it ignores environmental costs, leading to less sustainable outcomes. Overall, the framework prioritizes realistic pit design over superior economic gains. By embedding environmental factors into mine planning, it enhances resource efficiency, minimizes ecological impacts, and promotes cleaner production, thereby advancing sustainability in mining through reinforcement learning-based optimization.