Development of approximate scheduling-adaptive controllers for multi-products continuous chemical processes using deep learning techniques and model predictive control

Recently, Machine learning (ML) techniques are increasingly used to enhance process control. However, most ML-based control solutions treat control problems in isolation from higher-level decision-making layers (scheduling in this study), which they must interact with and adapt to during operation. Consequently, they often become ineffective or inapplicable when scheduling scenarios vary (e.g., product types, sequences, quantities, or qualities).

Therefore, this work proposes a new Deep Learning-based Scheduling-Adaptive Controller (DL-SAC) that approximates Model Predictive Control (MPC) solutions while explicitly incorporating scheduling-layer decisions. DL-SAC learns how variations in product sequence, production rates, and quality specifications influence optimal closed-loop control actions. It is trained using a dataset generated by solving the nonlinear MPC problem under diverse scheduling scenarios. Each training instance includes state and control trajectories along with scheduling features such as production rates and product quality specifications, thereby embedding scheduling-contextual information into the control approximation.

The proposed approach is validated on a benchmark multi-product continuous chemical process subject to various scheduling configurations and process disturbance. Across these scenarios, DL-SAC achieves a Normalized Root Mean Square Error (NRMSE) of 1.19 % in predicting control actions, while reducing the online computational time required to solve the MPC problem by approximately 98.8 %. These results demonstrate the method’s capability to deliver accurate, real time control approximations while maintaining adaptability to variations in scheduling decisions and process dynamics. The approach (i) enhances real-time operational flexibility and adaptability of chemical plants and (ii) provides basis for improved integration between control and scheduling, enabling more unified and responsive process optimization.