Data-driven nonlinear system identification and SIR particle filtering for chemical process monitoring and prediction

Chemical process monitoring is essential for product quality, plant efficiency, and safety. Conventional methods often prove inaccurate, particularly when dealing with nonlinear process behaviour. This paper presents a new approach that combines data-driven nonlinear system identification using smoothed L-1 regularisation and a state prediction method using a sequential importance resampling (SIR) particle filter to provide a basis for process monitoring. The results obtained from the polycondensation reaction in an operator training simulator (OTS) with real process conditions validate the effectiveness of the method in detecting anomalies, addressing challenges in nonlinear process modeling, and reliable state prediction for chemical process monitoring. Copyright (C) 2024 The Authors.