Enhancing polymeric nano-composite ceramic membrane performance and sustainable recovery for palm oil mill effluent (POME) wastewater treatment using advanced chemometric algorithms

This study investigates the enhancement of emulsified oily wastewater treatment using high-performance poly (diallyldimethylammonium chloride) PDADMAC ultrafiltration membranes through a multi-model machine learning (ML) approach. The study was based on experimental scenarios and more emphasis on computational learning applications. In this context, kernel Gaussian Process Regression (GPR), Linear Regression (LR), Stepwise Regression (SWR), and Multiple Linear Regression (MLR) were employed to predict water flux (WF) and oil rejection (OR). Subsequently, traditional Response Surface Methodology (RSM) was developed for predictive comparison. The predictive skills were evaluated and visualized using statistical indicators and 2-dimensional diagrams. GPR achieved the highest predictive accuracy for OR, with an NSE of 99.32 %, zero bias (PBIAS 0.0000), and the lowest MAE (0.0010). For WF, the RSM-W) model outperformed others with an NSE of 82.03 %, the lowest MAE (0.0051), and a slight underestimation bias (PBIAS −0.0587). These models significantly outperformed RLR, SWR, and MLR, which showed moderate accuracy and higher prediction errors. The environmental implications align with the goals of the Environmental Protection Agency (EPA) and the United Nations Sustainable Development Goals (SDGs). Enhanced treatment processes contribute to cleaner water bodies, protect marine ecosystems, and promote sustainable industrial practices. Future research should focus on field trials to validate these models under real-world conditions, integration with real-time monitoring systems for dynamic adjustments, and life cycle assessments to evaluate long-term sustainability.