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This research explores the challenges of control and dynamic optimization in SAG mill grinding circuits, focusing on the interaction between mill load and breakage rates. It evaluates the performance of three control methods: a model-free reinforcement learning algorithm and two model-based methods, aiming to maximize throughput while maintaining particle size distribution. The study emphasizes the potential of combining simulation modeling with data-driven approaches for real-time optimization in grinding

Author

王柏昆

Language

EN