Artificial intelligence-aided preparation of perovskite SrFexZr1-xO3-δ catalysts for ozonation degradation of organic pollutant concentrated water after membrane treatment. (March 2023)
- Record Type:
- Journal Article
- Title:
- Artificial intelligence-aided preparation of perovskite SrFexZr1-xO3-δ catalysts for ozonation degradation of organic pollutant concentrated water after membrane treatment. (March 2023)
- Main Title:
- Artificial intelligence-aided preparation of perovskite SrFexZr1-xO3-δ catalysts for ozonation degradation of organic pollutant concentrated water after membrane treatment
- Authors:
- Wang, Xu
Zhang, Yanan
Zhang, Cheng
Wei, Huangzhao
Jin, Haibo
Mu, Zhao
Chen, Xiaofei
Chen, Xinru
Wang, Ping
Guo, Xiaoyan
Ding, Fuchen
Liu, Xiaowei
Ma, Lei - Abstract:
- 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 SrFex Zr1-x O3-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 SrFe0.13 Zr0.87 O3-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 SrFe0.13 Zr0.87 O3-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. Graphical abstract: Image 1 Highlights: Preparation of perovskite SrFe x Zr 1-x O 3-δAbstract: 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 SrFex Zr1-x O3-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 SrFe0.13 Zr0.87 O3-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 SrFe0.13 Zr0.87 O3-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. Graphical abstract: Image 1 Highlights: Preparation of perovskite SrFe x Zr 1-x O 3-δ catalysts using a modified co-precipitation method. Cross-validation using neural network models (AI) and experimental data to optimize catalysts-. Reduction treatment improves SrFe 0.13 Zr 0.87 O 3-δ oxygen vacancies and catalytic activity. SrFe 0.13 Zr 0.87 O 3-δ generates reactive radicals to improve the reaction rate of CWOO. … (more)
- Is Part Of:
- Chemosphere. Volume 318(2023)
- Journal:
- Chemosphere
- Issue:
- Volume 318(2023)
- Issue Display:
- Volume 318, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 318
- Issue:
- 2023
- Issue Sort Value:
- 2023-0318-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-03
- Subjects:
- Artificial intelligence (AI) -- Perovskite -- CWOO -- TOC -- Membrane
Pollution -- Periodicals
Pollution -- Physiological effect -- Periodicals
Environmental sciences -- Periodicals
Atmospheric chemistry -- Periodicals
551.511 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00456535/ ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.chemosphere.2023.137825 ↗
- Languages:
- English
- ISSNs:
- 0045-6535
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3172.280000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 25668.xml