Modeling framework for batch-dependent dynamics of reaction process by combining first principles and machine learning

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.