In this study, we propose a novel methodology for the automatic control of heap leaching by means of policies obtained using Reinforcement Learning (RL). This methodology models the leaching dynamics as a Markov Decision Process (MDP) whose reward function captures the economic profit of the heap leaching operation. As a case study, the leaching process of copper oxide heaps is simulated and controlled under various conditions. Results show that controlling this process using the proposed approach outperforms a heuristic control strategy that emulates real mining operations by increasing recovery rates by 2.25 times, reducing water consumption by 32.4% and acid consumption by 19.9%, and enhancing economic returns by 17.5%. This approach highlights the robustness of a Deep Reinforcement Learning (DRL) policy in heap leaching operations under significant out-of-distribution (OOD) conditions, demonstrating its adaptability and effectiveness under diverse and unpredictable conditions. Furthermore, this research highlights the potential for this methodology to be applied to other leachable ores, as it could reduce the overall environmental impact of this operation by using fewer resources.

中文翻译：

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使用深度强化学习控制堆浸桩

在这项研究中，我们提出了一种通过强化学习（RL）获得的策略自动控制堆浸的新方法。该方法将浸出动力学建模为马尔可夫决策过程（MDP），其奖励函数捕获堆浸操作的经济利润。作为案例研究，在不同条件下模拟和控制氧化铜堆的浸出过程。结果表明，使用所提出的方法控制该过程优于模拟真实采矿作业的启发式控制策略，将回收率提高了 2.25 倍，减少了 32.4% 的水消耗和 19.9% 的酸消耗，并提高了 17.5% 的经济效益。这种方法突出了深度强化学习（DRL）策略在显着分布外（OOD）条件下的堆浸操作中的稳健性，证明了其在各种和不可预测的条件下的适应性和有效性。此外，这项研究强调了这种方法应用于其他可浸出矿石的潜力，因为它可以通过使用更少的资源来减少该作业的总体环境影响。