Performance investigation of osmotically assisted reverse osmosis using explainable machine learning models: A comparative study

Abstract

Osmotically assisted reverse osmosis (OARO) can treat highly concentrated water and achieve higher recovery rates than reverse osmosis (RO). However, existing mathematical models cannot satisfactorily explain OARO systems due to certain limitations. To explore OARO more dynamically, we aimed to analyze OARO performance based on the laboratory-scale data acquired under different operating conditions, by implementing six machine learning (ML) models with the OARO data, evaluating their predictive capabilities, and investigating the effects of input variables on output variables using Shapley Additive Explanation (SHAP) analysis. In predicting the OARO outputs, the ensemble ML models outperformed the conventional ML models. Incorporating FO/RO membrane datasets significantly enhanced the ML model accuracy and mitigated overfitting issues. Regarding the prediction of water flux, the adjusted determination coefficient values increased by 0.478 for Random Forest and 0.569 for Extreme Gradient Boost in the test step. The SHAP analysis consistently ranked the importance of the input variables for OARO outputs. It revealed the presence of potentially influential but previously unrecognized input variables, such as temperature, for the draw concentration. The results of this study are expected to provide a better understanding of OARO.