Enhanced understanding of osmotic membrane bioreactors through machine learning modeling of water flux and salinity

Mathematical modeling can be helpful to understand and optimize osmotic membrane bioreactors (OMBR), a promising technology for sustainable wastewater treatment with simultaneous water recovery. Herein, seven machine learning (ML) algorithms were employed to model both water flux and salinity of a lab-scale OMBR. Through the optimum hyperparameters tuning and 5-fold cross-validation, the ML models have achieved more accurate results without obvious overfltting and bias. The median R-2 scores of water flux modeling were all over the 0.95 and the most of median R-2 scores from total dissolved solids (TDS) modeling were higher than 0.90. During model testing, random forest (RF) algorithm presented the highest R-2 score of 0.987 with the lowest root mean square error (RMSE = 0.044) for the water flux modeling, and extreme gradient boosting (XGB) algorithm exhibited the best results (R-2 = 0.97; RMSE = 0.234) in the TDS modeling. The Shapley Additive exPlanations (SHAP) analysis found that the phosphorus concentration was a critical input feature for both water flux and TDS modeling. Finally, the selected ML models were used to predict water flux and salinity affected by two input features and the predication results conflrmed the importance of the phosphate concentration. The results of this study have demonstrated the promise of ML modeling for investigating OMBR systems.

Automated summary

This study utilizes machine learning algorithms to mathematically model osmotic membrane bioreactors (OMBRs) and demonstrates their effectiveness in predicting water flux and salinity, with phosphorus concentration being a critical input feature.