EXPERIMENTAL AND COMPUTATIONAL ANALYSIS FOR OPTIMIZATION OF SEAWATER BIODEGRADABILITY USING PHOTO CATALYSIS

Seawater pollution is a significant global environmental problem. Various technologies and methods have been used to remove the contaminants found in saltwater. This experimental study investigates the degradation of contaminants present in seawater using solar photocatalysis, where a combination of TiO2 and ZnO was used. The effects of catalyst dosage, pH, and reaction duration were assessed using percentage removal efficiencies of total organic carbon (TOC), chemical oxygen demand (COD), biological oxygen demand (BOD), and biodegradability (BOD/COD). Biodegradability is essential for removing pollutants from saltwater and plays a vital role. The higher the biodegradability, the more efficient the treatment procedure will be. The most effective percentage reduction rates from the experimental data obtained were TOC=59.80%, COD=75.20%, BOD=23.94%, and biodegradability=0.055. For modeling, optimizing, and assessing the effects of parameters, the Design Expert based on Box Behnken design (RSM-BBD) and a predictive model based on the MATLAB adaptive neuro-fuzzy inference system (ANFIS) tools were used. The coefficient of determination R2 was found to be 0.977 for the RSM-BBD model and 0.99 for the ANFIS model. According to the RSM-BBD design, the maximum percentage pollutant elimination efficiencies were found to be TOC=55.4, COD=73.4, BOD=23.70%, and BOD/COD=0.054, but for the ANFIS model, they were TOC=59.4, COD=75.4, BOD=24.1%, and BOD/COD=0.055. It was discovered that the ANFIS model outperformed RSM-BBD in process optimization.