Artificial intelligence-aided preparation of perovskite SrFexZr1-xO3-δ catalysts for ozonation degradation of organic pollutant concentrated water after membrane treatment

Abstract

Membrane technology has been widely used to treat wastewater from a variety of industries, but it also results in a large amount of concentrated wastewater containing organic pollutants after membrane treatment, which is challenging to decompose. Here in this work, a series of perovskite SrFexZr1-xO3-δ catalysts were prepared via a modified co-precipitation method and evaluated for catalytic ozone oxidative degradation of m-cresol. An artificial neural intelligence networks (ANN) model was employed to train the experimental data to optimize the preparation parameters of catalysts, with SrFe0.13Zr0.87O3-δ being the optimal catalysts. The resultant catalysts before and after reduction were then thoroughly characterized and tested for m-cresol degradation. It was found that the co-doping of Fe and Zr at the B-site and the improvement of oxygen vacancies and oxygen active species by reduction dramatically increased TOC removal rates up to 5 times compared with ozone alone, with the conversion rate of m-cresol reaching 100%. We also proposed a possible mechanism for m-cresol degradation via investigating the intermediates using GC-MS, and confirmed the good versatility of the reduced SrFe0.13Zr0.87O3-δ catalyst to remove other common organic pollutants in concentrated wastewater. This work demonstrates new prospects for the use of perovskite materials in wastewater treatment.