Developing an efficient explainable artificial intelligence approach for accurate reverse osmosis desalination plant performance prediction: application of SHAP analysis

In recent decades, securing drinkable water sources has become a pressing concern for populations in various regions worldwide. Therefore, to address the growing need for potable water, contemporary water purification technologies can be employed to convert saline sources into drinkable supplies. Therefore, the prediction of important parameters of desalination plants is a key task for designing and implementing these facilities. In this regard, artificial intelligence techniques have proven to be powerful assets in this field. These methods offer an expedited and effective means of estimating effective parameters, thus catalyzing their implementation in real-world scenarios. In this study, the predictive accuracy of six different machine learning models, including Natural Gradient-based Boosting (NGBoost), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Support vector regression (SVR), Gaussian Process Regression (GPR), and Extremely Randomized Tree (ERT) was evaluated for modelling the parameter of permeate flow as a key element in system efficiency, energy consumption, and water quality using six various input combinations of feed water salt concentration, condenser inlet temperature, feed flow rate, and evaporator inlet temperature. The next phase of this research employed the SHAP interpretability method to illustrate the impact of individual variables on the model's output. Moreover, the predictive performance of the developed frameworks was evaluated using a set of five dependable statistical measures: RMSE, NS, MAE, MAPE and R2. These indicators were utilized to provide a robust means of gauging the precision of the model's forecasts. A comparative analysis of the outcomes, as measured by the RMSE criteria, revealed that the SVR technique (RMSE = 0.125 L/(h<middle dot>m2)) exhibited superior performance compared to NGBoost (RMSE = 0.163 L/(h<middle dot>m2)), AdaBoost (RMSE = 0.219 L/(h<middle dot>m2)), CatBoost (RMSE = 0.149 L/(h<middle dot>m2)), GPR (RMSE = 0.156 L/(h<middle dot>m2)), and ERT (RMSE = 0.167 L/(h<middle dot>m2)) methodologies in predicting permeate flow rates. The modelling outcomes obtained during the evaluation stage demonstrated the efficacy of the SVR algorithm in enhancing the precision of permeate flow forecasts, utilizing relevant input variables.