Artificial intelligence in wastewater treatment: A data-driven analysis of status and trends.

Journal: Chemosphere
Published Date:

Abstract

Wastewater treatment is a complex process that involves many uncertainties, leading to fluctuations in effluent quality and costs, and environmental risks. Artificial intelligence (AI) can handle complex nonlinear problems and has become a powerful tool for exploring and managing wastewater treatment systems. This study provides a summary of the current status and trends in AI research as applied to wastewater treatment, based on published papers and patents. Our results indicate that, at present, AI is primarily used to evaluate removal of pollutants (conventional, typical, and emerging contaminants), optimize models and process parameters, and control membrane fouling. Future research will likely continue to focus on removal of phosphorus, organic pollutants, and emerging contaminants. Moreover, analyzing microbial community dynamics and achieving multi-objective optimization are promising directions of research. The knowledge map shows that there may be future technological innovation related to predicting water quality under specific conditions, integrating AI with other information technologies and utilizing image-based AI and other algorithms in wastewater treatment. In addition, we briefly review development of artificial neural networks (ANNs) and explore the evolutionary path of AI in wastewater treatment. Our findings provide valuable insights into potential opportunities and challenges for researchers applying AI to wastewater treatment.

Authors

  • Shubo Zhang
    Affiliated Hospital of North China University of Science and Technology, Tangshan, 063009, China. Electronic address: nmd955703@163.com.
  • Ying Jin
    Department of Immunology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China.
  • Wenkang Chen
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, China.
  • Jinfeng Wang
  • Yanru Wang
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, China.
  • Hongqiang Ren
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, Jiangsu, China.