Prediction and Prognosis of Incipient Off-Spec Events in Diacetone Alcohol Production Process using Hierarchical Process Monitoring

This work considers the application of a hierarchical process monitoring approach to incipient prediction and prognosis of off-spec events in a dynamic simulation-based diacetone alcohol (DAA) production process. The process encapsulates many complexities that are present in industrial processes, such as the presence of recycle, control loops, nonlinearities, etc., and thus can be used to investigate the monitoring performance of data-driven methods. The hierarchical process monitoring approach is based on principal component analysis (PCA) and slow feature analysis (SFA) which are unsupervised learning methods. The approach has the ability to extract relevant features from high-dimensional datasets while also incorporating dynamic variation of the variables in the analysis. The study consists of several scenarios based on step and pulse disturbances in various units of the process. The simulation results show that the hierarchical process monitoring approach accurately predicts the incipient off-spec events well in advance, and also identifies responsible units with sufficient accuracy.