Solar photocatalytic degradation of seawater organic pollutants using RSM process optimization and advanced machine learning modeling

Abstract

Water contamination caused by organic pollutants leads to severe deterioration of water quality and major environmental concern worldwide. The implementation of effective monitoring and remediation measures is necessary for sustainable water treatment. A batch reactor using titanium oxide (TiO2) as a catalyst to remove total organic carbon (TOC) and chemical oxygen demand (COD) in seawater by solar photocatalysis was used. Response surface methodology was carried out with input parameters such as TiO2 dosage 1–4 g/L, pH value 6–9 and reaction time 60–300 min and percentage reduction of TOC and COD as output variables. Adaptive Boosting (AdaBoost), Gradient Boosting (GB), Bayesian Ridge Regression (BRR) and Long Short-Term Memory networks (LSTM) were used for prediction. The accuracy of models is determined by metric analysis. GB model recorded the highest R2=0.993 (COD) and 0.980 (TOC) with minimal error MSE of 3.43 COD and 0.009 TOC. Whereas RSM showed R2=0.953, 0.950 for COD and TOC. AdaBoost demonstrated R2 of 0.973 of COD, in addition BRR and LSTM showed lower prediction accuracy. The findings shed light on the possibility of machine learning methods in improving prediction of water quality parameters and optimizing of water treatment processes.