Based on machine learning: Energy consumption optimization and energy efficiency evaluation for photovoltaic electro-fenton technology in wastewater treatment plant

Wei Zhang, Xiding Zeng, Yuan Huang, Juan Liang, Xinyu Wang, Jiahong Guo, Zhangyu Li, Kun Yang and Jing Zhang

Renewable Energy, 2025, vol. 243, issue C

Abstract: The application of photovoltaic (PV) technology in wastewater treatment plants (WWTPs) holds enormous potential as it provides renewable energy and can significantly reduce energy consumption and operation costs. However, it is a crucial and challenging issue to accurately and effectively assess PV technology's consumption optimization and energy efficiency in WWTPs. This study chose the PV-Electro-Fenton process as an example. The PV-Electro-Fenton process's energy consumption and pollutant degradation efficiency were predicted based on the machine learning model and optimization methods. Regression models were established for current intensity, electrolyte concentration, and iron ion dosage parameters based on experiments with different single variables. The trained artificial neural network model accurately predicted this process's degradation efficiency and energy consumption (R = 0.985, MSE = 1.57), which was further validated in actual WWTPs. Additionally, typical WWTPs in different regions with various solar radiation resources worldwide were selected to assess the energy-saving potential of PV-supported WWTPs. This research provides an essential reference for energy management and feasibility design of PV-supported WWTPs.

Keywords: Wastewater treatment; Renewable energy; Machine learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:243:y:2025:i:c:s0960148125002885

DOI: 10.1016/j.renene.2025.122626

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