Application and innovation of artificial intelligence models in wastewater treatment

人工神经网络 自适应神经模糊推理系统 人工智能 污水处理 废水 污染物 计算机科学 水质 过程(计算) 生化需氧量 生化工程 机器学习 环境科学 化学需氧量 模糊逻辑 环境工程 工程类 生态学 模糊控制系统 生物 操作系统
作者
Wenlong Xu,Yajun Wang,Yitong Wang,Junguo Li,Ya-Nan Zeng,Huawei Guo,Huan Liu,Kai-Li Dong,Liang-Yi Zhang
出处
期刊:Journal of Contaminant Hydrology [Elsevier BV]
卷期号:267: 104426-104426 被引量:6
标识
DOI:10.1016/j.jconhyd.2024.104426
摘要

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.
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