A Hybrid Modeling Approach to Predict Pollutant Scrubber Remaining Useful Life

Chemical plants require reliable systems for pollutant abatement. These processes often operate under cyclic abatement and regeneration cycles over extended periods of time. Throughout this period, the abatement systems experience a multitude of phenomena that may degrade performance in a fashion that is challenging to predict by first-principle models. These complex phenomena offer an opportunity to leverage data-driven models. To improve their predictive ability, data driven models can be complemented with physics -based information that constrains modeling results. In this contribution, we describe a hybrid modeling approach where physics -derived features are developed to enable data -driven models to effectively predict the performance of real pollutant abatement systems in the Dow Chemical Company.