College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
J Contam Hydrol. 2024 Nov;267:104426. doi: 10.1016/j.jconhyd.2024.104426. Epub 2024 Sep 6.
At present, as the problem of water shortage and pollution is growing serious, it is particularly important to understand the recycling and treatment of wastewater. Artificial intelligence (AI) technology is characterized by reliable mapping of nonlinear behaviors between input and output of experimental data, and thus single/integrated AI model algorithms for predicting different pollutants or water quality parameters have become a popular method for simulating the process of wastewater treatment. Many AI models have successfully predicted the removal effects of pollutants in different wastewater treatment processes. Therefore, this paper reviews the applications of artificial intelligence technologies such as artificial neural networks (ANN), adaptive network-based fuzzy inference system (ANFIS) and support vector machine (SVM). Meanwhile, this review mainly introduces the effectiveness and limitations of artificial intelligence technology in predicting different pollutants (dyes, heavy metal ions, antibiotics, etc.) and different water quality parameters such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) in wastewater treatment process, involving single AI model and integrated AI model. Finally, the problems that need further research together with challenges ahead in the application of artificial intelligence models in the field of environment are discussed and presented.
目前，随着水资源短缺和水污染问题的日益严重，了解废水的循环利用和处理显得尤为重要。人工智能（AI）技术的特点是能够可靠地映射实验数据输入和输出之间的非线性行为，因此，用于预测不同污染物或水质参数的单一/集成 AI 模型算法已成为模拟废水处理过程的一种流行方法。许多 AI 模型已经成功地预测了不同废水处理过程中污染物的去除效果。因此，本文综述了人工智能技术在预测不同废水处理过程中不同污染物（染料、重金属离子、抗生素等）和不同水质参数（生化需氧量（BOD）、化学需氧量（COD）、总氮（TN）和总磷（TP））方面的应用。同时，主要介绍了人工智能技术在预测不同污染物（染料、重金属离子、抗生素等）和不同水质参数（生化需氧量（BOD）、化学需氧量（COD）、总氮（TN）和总磷（TP））方面的有效性和局限性，涉及单一 AI 模型和集成 AI 模型。最后，讨论并提出了人工智能模型在环境领域应用中需要进一步研究的问题和面临的挑战。
