Machine Learning-Driven Optimisation of Operational Spaces for Uncertainty Management in Process Industries

Process optimisation and quality control are crucial in process industries to minimise waste and enhance economics. However, common uncertainties ranging from feedstock variability to human error can cause significant deviations in product quality leading to batch discards. This study introduces a novel framework combining machine learning with optimisation strategies to identify optimal operational spaces under uncertainty. Using a process model, the framework screens a broad operational space, isolating promising sub-regions and control trajectories. Machine learning techniques are used to cluster these sub-regions by displayed control patterns, and a dynamic optimisation framework identifies the maximum operable design space, ensuring constraints are met under uncertainty. Two case studies, involving a fermentation process and a formulation manufacturing process, were conducted to demonstrate the high efficiency of the proposed framework and to showcase its strong potential for industrial applications.