A machine learning framework for improving refinery production planning

We propose a framework that relies on machine learning techniques and statistical modeling to enhance industrial production planning. Supervised learning is employed to improve the production planning model, whereas unsupervised learning is used to achieve economic synchronization between the process control and production planning layers. Finally, an upgraded production planning decision‐making structure is formulated where model uncertainty, the effect of process control/disturbances, and time correlation are considered. The proposed framework is implemented on an industry‐relevant refinery model demonstrating that the performance of the framework is substantially better than established industrial production planning techniques.