Machine Learning-Aided Robust Optimisation for Identifying Optimal Operational Spaces under Uncertainty

Process optimisation and quality control are crucial in process industries for minimising product waste and improving plant economics. Identifying robust operational regions that ensure both product quality and performance is particularly valued in industries. However, this task is complicated by operational uncertainties, which can lead to violations of product quality constraints and significant batch discards. We propose a novel robust optimisation strategy that integrates advanced machine learning and process systems engineering to systematically identify optimal operational regions under uncertainty. Our approach begins by using a process model to screen a broad operational space across various uncertainty scenarios, pinpointing promising control trajectories to satisfy process constraints and product quality. Machine learning is then employed to cluster these trajectories into sub-regions. Finally, a two-layer dynamic optimisation framework is employed to determine the optimal control trajectory and corresponding operable space within each promising sub-region. To demonstrate the efficiency of our approach, we used a case study focusing on the quality control of a dynamic batch process for formulation product manufacturing. The resulting operational regions were shown to meet product quality demands and offer a significant improvement in optimality over the current operation, highlighting the advantage and industrial potential of our strategy.