Stabilized oily-wastewater separation based on superhydrophilic and underwater superoleophobic ceramic membranes: Integrated experimental design and standalone machine learning algorithms

Background: Reliable computational approaches to evaluate ceramic membrane performance in wastewater treatment mark a transformative step towards optimizing separation processes, ensuring environmental sustainability, and advancing water purification technologies. The current study explores the influential factors using artificial intelligence (AI) tools in the performance evaluation of superhydrophilic and underwater super-oleophobic ceramic membranes for the selective treatment of oily wastewater. Methods: The chemometrics scenario of the research based on established experimental work employs advanced AI models viz: Random Forest (RF), Support Vector Regression (SVR), and Gaussian Process Regression (GPR) to predict the efficacy of these membranes in terms of rejection and flux. The model predictions were evaluated using the Pearson Correlation Coefficient (PCC), Willmott Index (WI), mean absolute percentage error (MAPE), and mean absolute error (MAE). Significant findings: From the results, GPR had shown good agreement with correlations (WI=99.9) during the training and testing phases for flux prediction, indicating an exceptional model fit with negligible error (MAPE=0.001, MAE=0.000 in the testing phase). For rejection modelling, GPR and SVR exhibit similar levels of accuracy, with moderate PCC and WI values, while RF reveals significant limitations with the lowest scores across all statistical metrics. The findings highlight the potential of AI in optimizing wastewater treatment processes, with GPR identified as the most promising model for flux prediction. This study would provide insight into the modelling of the membrane separation process for oily wastewater and integrate AI in the performance evaluation of wastewater reclamation.