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Research progress of the artificial intelligence application in wastewater treatment during 2012-2022: a bibliometric analysis.
Yu, Xiaoman; Chen, Shuai; Zhang, Xiaojiao; Wu, Hongcheng; Guo, Yaoguang; Guan, Jie.
Afiliación
  • Yu X; School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China E-mail: guanjie@sspu.edu.cn.
  • Chen S; School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China; Anhui International Joint Research Center for Nano Carbon-based Materials and Environmental Health, Huainan 232001, China.
  • Zhang X; School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China.
  • Wu H; Shanghai Wobai Environmental Development Co. Ltd, Shanghai 201209, China.
  • Guo Y; School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China.
  • Guan J; School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China.
Water Sci Technol ; 88(7): 1750-1766, 2023 Oct.
Article en En | MEDLINE | ID: mdl-37830995
ABSTRACT
This study identified literatures from the Web of Science Core Collection on the application of artificial intelligence in wastewater treatment from 2011 to 2022, through bibliometrics, to summarize achievements and capture the scientific and technological progress. The number of papers published is on the rise, and especially, the number of papers issued after 2018 has increased sharply, with China contributing the most in this regard, followed by the US, Iran and India. The University of Tehran has the largest number of papers, WATER is the most published journal, and Nasr M has the largest number of articles. Collaborative network has been developed mainly through cooperation between European countries, China and the US. Remote sensing in developing countries needs to be further integrated with water quality monitoring programs. It is worth noting that artificial neural network is a research hotspot in recent years. Through keyword clustering analysis, 'machine learning' and 'deep learning' are hot keywords that have emerged since 2019. The use of neural networks for predicting the effectiveness of treatment of difficult to degrade wastewater is a future research trend. The rapid advancement of deep learning provides the opportunity to build automated pipeline defect detection systems through image recognition.
Asunto(s)

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Purificación del Agua País/Región como asunto: Asia Idioma: En Revista: Water Sci Technol Asunto de la revista: SAUDE AMBIENTAL / TOXICOLOGIA Año: 2023 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Purificación del Agua País/Región como asunto: Asia Idioma: En Revista: Water Sci Technol Asunto de la revista: SAUDE AMBIENTAL / TOXICOLOGIA Año: 2023 Tipo del documento: Article