We propose a modeling framework for automating batch processes operation. Batch processes are often controlled by PID controllers, where engineers manually regulate their parameters and temporal patterns of reference signals. Therefore, it takes a long time for optimizing these parameters and temporal patterns. A possible solution for this is to apply so-called Model Predictive Control (MPC) technology to the tuning. Here, batch process dynamics depend on the types of products and of equipment, thereby forcing engineers to construct and maintain multiple models that correspond to the number of combinations of product types and equipment types. Thus, batch process modeling is a time-consuming and complicated task. To solve this problem, we propose a modeling framework; about a modeling target, the part applying commonly and parameters can be decided in advance are constructed by mathematical models, and the part that required experimentation for designing or tuning are constructed by machine learning models. We expect this framework can improve estimation accuracy and suppressing the number of model construction by separating model construction and combining the mathematical and machine learning models. In our simulation, we confirmed that our proposed model can suppress prediction error (RMSE) of reactor temperature under 1 K. Furthermore, an optimization algorithm with our model can find a temporal pattern of a reference signal so as to reduce control error of reactor temperature under 1.99 K.

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结合第一原理和机器学习的反应过程批次相关动力学建模框架

我们提出了一个用于自动化批处理操作的建模框架。批处理通常由 PID 控制器控制，工程师手动调节其参数和参考信号的时间模式。因此，优化这些参数和时间模式需要很长时间。一个可能的解决方案是将所谓的模型预测控制（MPC）技术应用于调整。在这里，批处理动态取决于产品和设备的类型，从而迫使工程师构建和维护与产品类型和设备类型的组合数量相对应的多个模型。因此，批处理建模是一项耗时且复杂的任务。为了解决这个问题，我们提出了一个建模框架；对于建模目标，通常应用和可以预先确定参数的部分是通过数学模型构建的，而需要进行设计或调整的实验的部分是通过机器学习模型构建的。我们期望该框架能够通过分离模型构建并结合数学和机器学习模型来提高估计精度并抑制模型构建的数量。在我们的模拟中，我们证实我们提出的模型可以将反应堆温度的预测误差（RMSE）抑制在1 K以下。此外，我们模型的优化算法可以找到参考信号的时间模式，从而减少反应堆温度的控制误差低于 1.99 K。