Combination of CFD and machine learning for improving simulation accuracy in water purification process via porous membranes

Abstract

Membrane system for molecular separation was studied in this work using combined modeling approach. Computational fluid dynamics (CFD) was conducted and integrated to machine learning models for description of ozonation process in membrane contactors. For the machine learning modeling, we investigated the performance of boosted models, specifically AdaBoost KNN, AdaBoost DT, and AdaBoost ARD, for predicting the concentration (C) of ozone using the input variables, i.e., r and z. The hyper-parameter optimization is done using Successive Halving in this study. The results reveal that AdaBoost KNN achieves the highest performance among the three models, with an impressive R2 score of 0.9992. This indicates an excellent fit of the model to the data, implying that approximately 99.92% of the variance in the concentration can be explained by the input variables r and z. Moreover, AdaBoost KNN demonstrates a low RMSE of 1.5695E-02, indicating its ability to provide accurate predictions with small deviations from the actual values. The maximum error of 1.02733E-01 further confirms the model's robustness, as it represents the largest deviation between predicted and CFD values, which is relatively small.