A mining complex is an integrated value chain where the materials extracted from a group of mineral deposits are sent to different processing streams to produce sellable products. A major short-term decision in a mining complex is to determine the flow of materials that first includes deciding which handling facilities to send the extracted materials and then determining how to utilize the processing facilities. The flow of materials through the mining complex is significantly dependent on the performance of and interaction between its different components. New digital technologies, including the development of advanced sensors and monitoring devices, have enabled a mining complex to acquire new information about the performance of its different components. This paper proposes a new continuous updating framework that combines policy gradient reinforcement learning and an extended ensemble Kalman filter to adapt the short-term flow of materials in a mining complex with incoming information. The framework first uses a new extended ensemble Kalman filter to update the uncertainty models of the different components of a mining complex with new incoming information. Then, the updated uncertainty models are fed to a neural network trained using a policy gradient reinforcement learning algorithm to adapt the short-term flow of materials in a mining complex. The proposed framework is applied to a copper mining complex and shows its ability to efficiently adapt the short-term flow of materials in an operational mining environment with new incoming information. The framework better meets the different production targets while improving the cumulative cash flow compared to industry standard approaches.

采矿综合体是一条集成的价值链，其中将从一组矿藏中提取的材料发送到不同的处理流以产生可出售的产品。采矿综合体的主要短期决策是确定物料流，该流程首先包括确定哪些处理设施发送提取的物料，然后确定如何利用处理设施。穿过采矿综合体的物料流动很大程度上取决于其不同组件的性能以及它们之间的相互作用。新的数字技术，包括先进传感器和监视设备的开发，使采矿综合体能够获取有关其不同组件性能的新信息。本文提出了一个新的持续更新框架，该框架结合了策略梯度强化学习和扩展的集成卡尔曼滤波器，以利用输入信息适应采矿企业中的短期物料流。该框架首先使用新的扩展集成卡尔曼滤波器，用新的输入信息更新采矿综合体不同组件的不确定性模型。然后，将更新后的不确定性模型馈送到使用策略梯度强化学习算法训练的神经网络，以适应采矿综合体中的短期物料流。拟议的框架已应用于铜矿开采综合体，并显示了其在运营采矿环境中利用新的传入信息有效适应短期物料流的能力。