Leveraging machine learning to model salinity and water flux for improved insights into forward osmosis membrane bioreactors

Forward osmosis (FO) and integrated FO with membrane bioreactors (FOMBR), have recently been recognized as highly effective solutions for water treatment and purification. FOMBR operation is intricate due to the combination of membrane filtration and biological treatment, along with the influence of various parameters. To improve the understanding of FOMBR for its potential applications, an accurate prediction model is crucial for enhancing performance and optimizing parameters. Therefore, this study employed various machine learning (ML) approaches to estimate the water flux (Jw) and total dissolved solids in a lab-scale FOMBR system. An extensive dataset was collected from the literature for model development. Different statistical metrics were used to assess the efficacy of the developed models. Two different explainability approaches, partial dependence plots (PDP) and SHapley Additive exPlanation (SHAP), were used to make the predictions of the ML models more explainable and transparent. Notably, the gradient boosting (GB) algorithm achieved an excellent R score of 0.99, while decision tree (DT) and support vector regression (SVR) achieved lower R scores of 0.9719 and 0.9701, respectively, in estimating water flux. Similarly, the GB model performed exceptionally well in estimating TDS of FOMBR. However, the DT and SVR models exhibited the lowest prediction performance compared to the other developed models in estimating the TDS. Overall, the GB algorithm and SVR integrated with boosting and bagging algorithms showed superior performance in predicting both TDS and water flux. In contrast, the standalone models, DT and SVR, provided poor prediction performance. The SHAP and PDP analyses demonstrated that phosphate level was the primary factor influencing both TDS and water flux.