Application and innovation of artificial intelligence models in wastewater treatment.

Journal: Journal of contaminant hydrology
Published Date:

Abstract

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.

Authors

  • Wen-Long Xu
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
  • Ya-Jun Wang
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
  • Yi-Tong Wang
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China. Electronic address: wangyt@ncst.edu.cn.
  • Jun-Guo Li
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
  • Ya-Nan Zeng
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
  • Hua-Wei Guo
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
  • Huan Liu
    Department of Chemical and Biochemical Engineering, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, Fujian, China.
  • Kai-Li Dong
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.
  • Liang-Yi Zhang
    College of Metallurgy and Energy, North China University of Science and Technology, 21 Bohai Street, Tangshan 063210, China.