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

• Published in: Water Science and Engineering Vol. 18; no. 3; pp. 324 - 334
• Main Authors: Palma, Davide, Antela, Kevin U., Prevot, Alessandra Bianco, Cervera, M. Luisa, Morales-Rubio, Angel, Sáez-Hernández, Roberto
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.09.2025; Elsevier
• Subjects: artificial intelligence; Artificial neural networks; Degradation; Machine learning; Optimization; Persistent organic pollutants; Wastewater; wastewater treatment; water; Degradation; Persistent organic pollutants; Wastewater; Optimization; Artificial neural networks; Machine learning
• ISSN: 1674-2370
• DOI: 10.1016/j.wse.2025.04.005

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.