In reverse osmosis seawater treatment process, membrane fouling can be mitigated by degrading organic pollutants present in the feed seawater. The present study evaluates the effectiveness of employing solar photocatalysis using TiO2/ZnO/H2O2 to pretreat reverse osmosis (RO) feed seawater under solar irradiation. Process optimisation and performance evaluation were undertaken using response surface methodology-desirability function and RSM integrated with genetic algorithm (RSM-GA). Statistical analysis was performed to determine the interactive relationships and main effects of input factors such as TiO2 dosage, H2O2 dosage, pH, reaction time and ZnO dosage. The performance evaluation was determined in terms of percentage removal of total organic carbon (TOC) and chemical oxygen demand (COD). The obtained optimum values using RSM-GA evaluation for TOC and COD removal were found to be 76.5% and 63.9%, respectively. The predicted RSM-GA results correspond well with the experimental results (TOC removal = 73.3%, COD removal = 61.2%). Utilization of renewable solar energy coupled with optimum utilisation of nanophotocatalysts enables this technique to be a unique treatment process for RO pretreatment of seawater and membrane fouling mitigation.

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The performance of desalination plants predominantly depends on the enhancement of membrane productivity through the effective removal of organic foulants from saline water prior to the membrane process. This research evaluates the performance of the ZnO-immobilized solar nanophotocatalytic process integrated with Fe 2+ /H 2 O 2 system for the removal of organics from reverse osmosis (RO) feed seawater. Machine-learning and response surface methodology (RSM) models were used for optimizing the performance of such a hybrid system in terms of five input factors: initial TOC (mg/L), pH, H 2 O 2 dosage (g/L), Fe 2+ dosage (mg/L) and solar irradiation time (minutes). Both machine-learning and RSM regression models were optimized using nondominated sorting genetic algorithm (NSGA-III) for estimating optimum organic degradation performance in terms of residual Fe 2+ , total organic carbon (TOC) removal and chemical oxygen demand (COD) removal. The response values obtained from the experimental run conducted at the optimum settings of ANN-NSGA-III was found to be TOC removal = 81.4%, COD removal = 77.4% and residual Fe 2+ = 1.95 mg/L. The pilot-scale solar nanophotocatalytic reactor optimized in the present research is worthy of being upscaled for wide application in desalination plants.