Advancing Sustainable Wastewater Treatment Using Enhanced Membrane Oil Flux and Separation Efficiency through Experimental-Based Chemometric Learning

Abstract

Efficient oil–water separation using membranes directly aligns with removing oil pollutants from water sources, promoting water quality. Hence, mitigating environmental harm from oil spills and contamination and fostering ecosystem health for sustainable development. Computational learning, such as artificial intelligence (AI), enhances membrane oil flux and separation efficiency by optimizing process parameters, leading to improved oil–water separation and aligning AI with sustainable environmental protection and resource efficiency solutions. This study employed phase-inversion coupled with sintering to create the ceramic membrane. The Stöber method was adopted to prepare the superhydrophobic silica sol-gel solutions. The data from the mentioned experiment were imposed into regression models, namely, multilinear regression analysis (MLR), support vector regression (SVR), and robust linear regression (RLR), to simulate three different scenarios (oil flux, separation efficiency, and oil flux and separation efficiency). The outcomes were validated and evaluated using several statistical (R2, MSE, R, and RMSE) and graphical visualizations. For oil flux, the results show that the most effective simulation was achieved in SVR-M2 and the statistical criteria for the testing phase were R2 = 0.9847, R = 0.9923, RMSE = 0.0333, and MSE = 0.0011. Similarly, SVR-M2 was superior to other modeling techniques for the separation efficiency in the testing phase (R2 = 0.9945, R = 0.9972, RMSE = 0.0282, MSE = 0.0008). Reliability outcomes promise to revolutionize how we model and optimize membrane-based oil–water separation processes, with implications for various industries seeking sustainable and efficient solutions.