Volume 18 Issue 3
Sep.  2025
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Article Navigation >  Water Science and Engineering  > 2025  >  18(3): 324-334
Davide Palma, Kevin U. Antela, Alessandra Bianco Prevot, M. Luisa Cervera, Angel Morales-Rubio, Roberto Sáez-Hernández. 2025: Artificial neural networks applied to photo-Fenton process: An innovative approach to wastewater treatment. Water Science and Engineering, 18(3): 324-334. doi: 10.1016/j.wse.2025.04.005
Citation: Davide Palma, Kevin U. Antela, Alessandra Bianco Prevot, M. Luisa Cervera, Angel Morales-Rubio, Roberto Sáez-Hernández. 2025: Artificial neural networks applied to photo-Fenton process: An innovative approach to wastewater treatment. Water Science and Engineering, 18(3): 324-334. doi: 10.1016/j.wse.2025.04.005
shu

Artificial neural networks applied to photo-Fenton process: An innovative approach to wastewater treatment

doi: 10.1016/j.wse.2025.04.005

  • a. Department of Chemistry, University of Turin, Via P. Giuria 5, Torino 10125, Italy;

  • b. Department of Analytical Chemistry, University of Valencia, Dr. Moliner 50, Burjassot 46100, València, Spain

  • Received Date: 2024-10-25
  • Accepted Date: 2025-03-28
    • Available Online: 2025-10-15
    Abstract
  • Artificial intelligence (AI) is a revolutionizing problem-solver across various domains, including scientific research. Its application to chemical processes holds remarkable potential for rapid optimization of protocols and methods. A notable application of AI is in the photo-Fenton degradation of organic compounds. Despite the high novelty and recent surge of interest in this area, a comprehensive synthesis of existing literature on AI applications in the photo-Fenton process is lacking. This review aims to bridge this gap by providing an in-depth summary of the state-of-the-art use of artificial neural networks (ANN) in the photo-Fenton process, with the goal of aiding researchers in the water treatment field to identify the most crucial and relevant variables. It examines the types and architectures of ANNs, input and output variables, and the efficiency of these networks. The findings reveal a rapidly expanding field with increasing publications highlighting AI's potential to optimize the photo-Fenton process. This review also discusses the benefits and drawbacks of using ANNs, emphasizing the need for further research to advance this promising area.

     

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