Performance prediction of a reverse osmosis system using machine learning

Abstract

The reverse osmosis process holds great importance in the water treatment industry. Despite its common use, this process suffers from membrane fouling, which affects the quality of the produced water and the performance of the membrane itself. So far, the operation of reverse osmosis systems relies on the operators’ experience, with maintenance activities carried out according to predefined schedules or criteria. This work involves developing a sliding mode observer-based fouling estimation, and using various machine learning techniques to provide real-time predictions and maintenance recommendations. The results provide valuable insights into the performance and suitability of these estimation approaches.