Artificial intelligence‐based multiobjective optimization of reverse osmosis desalination pretreatment using a hybrid ZnO‐immobilized/photo‐Fenton process

• Published in: Journal of chemometrics Vol. 36; no. 8
• Main Authors: Joy, Varghese Manappallil, Feroz, Shaik, Dutta, Susmita
• Format: Journal Article
• Language: English
• Published: Chichester Wiley Subscription Services, Inc 01-08-2022
• Subjects: Artificial intelligence; Chemical oxygen demand; Desalination; Desalination plants; Genetic algorithms; Hybrid systems; Hydrogen peroxide; Irradiation; Machine learning; membrane fouling; Membrane processes; Membranes; Multiple objective analysis; NSGA‐III; Optimization; Organic carbon; Osmosis; Performance degradation; Performance evaluation; photo‐Fenton process; Radiation dosage; Regression models; Response surface methodology; Reverse osmosis; Saline water; Seawater; seawater pretreatment; solar nanophotocatalysis; Solar radiation; Sorting algorithms; Zinc oxide
• ISSN: 0886-9383; 1099-128X
• DOI: 10.1002/cem.3434

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 Fe2+/H2O2 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, H2O2 dosage (g/L), Fe2+ 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 Fe2+, 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 Fe2+ = 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.
ZnO catalyst was efficiently immobilized on borosilicate glass tubes, and the morphology was analysed using SEM. Seawater organics was effectively removed using a hybrid solar/ZnO/photo‐Fenton process. Near‐global‐optimal solution is achieved using RSM‐ANN‐NSGA‐III paradigm.