Metronidazole photocatalytic degradation by zinc oxide nanoparticles synthesized in watermelon peel extract; Advanced optimization, simulation and numerical models using machine learning applications

Abstract

Antibiotics in water systems and wastewater are among the greatest major public health problem and it is global environmental issues. Herein a novel approach for the photocatalytic degradation of metronidazole (MTZ) by using eco-green zinc oxide nanoparticles (EG-ZnO NPs) which biosynthesised using watermelon peels extracts has been investigated. Mathematical prediction models using an adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and response surface methodology (RSM) were used to determine the optimal conditions for the degradation process. The FESEM analysis revealed that EG-ZnO NPs was white with a spherical shape and size between 40 and 88 nm. The simulation process for the mathematical prediction model revealed that the best validation performance was 55.35 recorded at epoch 2, the coefficient (R2) was 0.9967 for training data, as detected using ANN analysis. The best operating parameters for MTZ degradation was predicted using RSM to be: 170 mg L−1 of EG-ZnO NPs, 20.61 mg 100 mL−1 of MTZ, 10 min exposure time, and a pH of 5, with 77.48 vs 78.14% corresponding to the predicted and empirically measured respectively. The photocatalytic degradation of MTZ was fitted with pseudo-first-order kinetic (R2 > 0.90). MTZ lost the antimicrobial activity against Bacillus cereus (B. cereus) and Escherichia coli (E. coli) after degradation with EG-ZnO NPs at the optimal conditions as determined in the optimization process. These findings reflect the important role ANFIS and ANN in predicting and optimising the efficacy of engineered nanomaterials, including EG-ZnO NPs, for antibiotic degradation.