From data to clean water: XGBoost and Bayesian optimization for advanced wastewater treatment with ultrafiltration

Water pollution remains a pressing global challenge, threatening human health and ecosystem stability. Ultrafiltration emerges as a vital technology in this contest, offering a powerful tool for contaminant removal and safeguarding clean water resources. Thus, the optimization of ultrafiltration processes holds paramount significance for efficient contaminant removal. This study revolutionizes wastewater treatment by introducing a hybrid machine learning approach that optimizes ultrafiltration processes for superior contaminant removal. Utilizing the powerful synergy between eXtreme Gradient Boosting (XGBoost) and Bayesian optimization, we developed predictive models with remarkable accuracy (R² values exceeding 99%) for post-treatment concentrations of metal ions, organic pollutants, and salts. This translates to precise control over the ultrafiltration process, driven by 4 key input variables: metal ion concentration, organic pollutants, salts, and applied pressure. The findings not only demonstrate the effectiveness of this hybrid approach but also pave the way for significant advancements in wastewater treatment strategies, ultimately contributing to cleaner water. This research marks a significant leap in machine learning applications for environmental challenges, paving the way for further advancements in wastewater treatment technology.