数字农科院2.0

Harnessing machine learning for energy optimization and intelligent process control in wastewater treatment

文献类型： 外文期刊

作者： Yibo Du;Tianlong Lei;Jie Jin;Boyu Chen;Xuanbei Wu;Nuan Yang;Yihong Ge;Yan Wang;Qifeng Tan;Dingrui Fan;Yunhui Lei;Ling Yao

作者机构：

关键词： Effluent prediction;Energy optimization;Machine learning;Photocatalysis;WWTPs

期刊名称： Journal of Water Process Engineering

ISSN： 2214-7144

年卷期： 2025 年 80 卷

页码：

收录情况： SCIE（2025版）

摘要： Faced with the global energy crisis and the dual challenges of high energy consumption and carbon emissions in the wastewater treatment sector, the development of intelligent and low-carbon technologies has become an urgent priority. In the context of photocatalytic water treatment, improving the design of high-performance photocatalysts and gaining a deeper understanding of catalytic mechanisms are essential for enhancing process efficiency. Moreover, the integration of artificial intelligence in wastewater treatment plants (WWTPs) can reduce human operational errors, improve effluent quality, and achieve cost-effective, high-efficiency operations. This review systematically summarizes the application of machine learning (ML) in material synthesis optimization for photocatalytic water purification and system control for energy-saving in WWTPs. Our analysis reveals that while ML has matured in handling high-dimensional and nonlinear system data for predictive tasks, the field faces significant challenges in data bias and model interpretability which limit its further real-time utilization. We conclude that future effort should focus on data quality, model interpretability and reproducibility, aiming to create a standardized benchmarks to bridge the gap between research and full-scale implementation.

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