Generalized reactor neural ODE for dynamic reaction process modeling with physical interpretability

Modeling is essential for designing, scaling up, controlling, and optimizing a reactor or process involving reactions. However, developing high-fidelity mechanistic models from first principles for reactor systems involving complex physiochemical phenomena is usually time- and resource-consuming. Therefore, machine learning models using data-driven methods can help in such cases to fill the gap between the complex system and our limited knowledge. Currently, most research works use generic off-the-shelf machine learning models to model reactor behavior. Such models frequently face problems related to data limitations, dynamics, model accuracy, and model interpretability. Considering the increasing need for data-driven models, especially in the fine chemicals and pharmaceutical industry, this work presents a new machine learning model architecture specially for the dynamic modeling of general flow reactors. Derived from the conventional residence time distribution reactor model, our generalized reactor neural ODE (GRxnODE) can achieve, without any prior knowledge of reaction kinetics, higher model accuracy, data efficiency, and model interpretability than commonly used data-driven models. The well-trained model can predict dynamic reactor response and learn reaction kinetics and reactor RTD from process data. The source codes of the model are publicly available.