he mathematical model provided consistent predictions and insights into internal module dynamics, while the RBFNN exhibited high computational efficiency. However, the RBFNN showed limitations in predicting recovery accuracy for operational ranges with insufficient data. To address this, we introduced a data distance index to assess the reliability of RBFNN predictions, particularly in extrapolation scenarios.
We then integrated the approaches using the mathematical model for data imputation to expand the RBFNN’s training dataset. The integrated model, retrained with augmented data, achieved an R2 of 0.9920 and an RMSE of 0.3414 LMH for water flux prediction.
This approach not only provides more reliable predictions but also enhances the understanding of key FO performance parameters through Shapley Additive exPlanations (SHAP) analysis.
This synergistic method facilitates efficient FO system design and operation by optimizing process parameters under diverse conditions.