Dynamic optimizers for complex industrial systems via direct data-driven synthesis

The chemical process industry (CPI) faces significant challenges in improving sustainability and efficiency while maintaining conservative principles for managing cost, complexity, and uncertainty. This work introduces a data-driven approach to dynamic real-time optimization (D-RTO) that addresses the aforementioned concerns by directly extracting process optimization policies from historical plant data. Our method constructs a value function to evaluate trajectory quality and employs weighted regression to derive improved policies. When applied to a plant-wide industrial process control problem, the proposed optimizer demonstrates superior performance in adapting to disturbances while maintaining stability and product quality. These results challenge conventional assumptions regarding the potential of data-driven optimization in the CPI. Although limitations exist due to the black-box nature of neural networks, this study presents a promising avenue for enhancing operational efficiency in industrial settings. The proposed approach offers a practical solution for process optimization, as it leverages readily available historical data and does not require extensive modeling efforts. By demonstrating significant efficiency improvement on a realistic industrial benchmark problem, this work paves the way for the adoption of data-driven optimization techniques in real-world CPI applications.