This study presents the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin integrates three key components of the closed-loop operation: (1) fuzzy logic for expert control, (2) a state-space model for regulatory control, and (3) a recurrent neural network to simulate the SAG mill process. The digital twin is combined with a statistical framework for automatically detecting process disturbances (or critical operations), which triggers model retraining only when deviations from expected behavior are identified, ensuring continuous updates with new data to enhance the SAG supervision. The model was trained with 68 h of operational industrial data and validated with an additional 8 h, allowing it to predict mill behavior within a 2.5-min horizon at 30-s intervals with errors smaller than 5%.

中文翻译：

---

为专家控制的 SAG 磨机提供具有自动干扰检测功能的数字孪生


本研究介绍了由专家系统控制的半自磨 （SAG） 磨机数字孪生的开发和验证。数字孪生集成了闭环操作的三个关键组件：（1） 用于专家控制的模糊逻辑，（2） 用于监管控制的状态空间模型，以及 （3） 用于模拟 SAG 磨机过程的递归神经网络。数字孪生与用于自动检测过程干扰（或关键操作）的统计框架相结合，只有在发现与预期行为的偏差时才会触发模型重新训练，确保使用新数据持续更新以加强 SAG 监督。该模型使用 68 小时的运行工业数据进行训练，并额外使用 8 小时进行验证，使其能够在 2.5 分钟内以 30 秒的间隔预测磨机行为，误差小于 5%。