This work describes the development and validation of a digital twin for a semi-autogenous grinding (SAG) mill controlled by an expert system. The digital twin consists of three modules emulating a closed-loop system: fuzzy logic for the expert control, a state-space model for regulatory control, and a recurrent neural network for the SAG mill process. The model was trained with 68 hours of data and validated with 8 hours of test data. It predicts the mill's behavior within a 2.5-minute horizon with a 30-second sampling time. The disturbance detection evaluates the need for retraining, and the digital twin shows promise for supervising the SAG mill with the expert control system. Future work will focus on integrating this digital twin into real-time optimization strategies with industrial validation.

翻译：本研究描述了一种由专家系统控制的半自磨机数字孪生系统的开发与验证。该数字孪生系统包含模拟闭环系统的三个模块：用于专家控制的模糊逻辑模块、用于调节控制的状态空间模型模块，以及用于半自磨机过程的循环神经网络模块。模型使用68小时数据进行训练，并通过8小时测试数据验证。该系统能以30秒采样周期预测未来2.5分钟内磨机的运行状态。扰动检测模块可评估模型再训练需求，该数字孪生系统在配合专家控制系统监控半自磨机方面展现出应用潜力。后续工作将聚焦于将该数字孪生系统集成到实时优化策略中，并进行工业现场验证。