School of Resources and Environmental Engineering, Shanghai Polytechnic University, Shanghai 201209, China E-mail:
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.
Water Sci Technol. 2023 Oct;88(7):1750-1766. doi: 10.2166/wst.2023.296.
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.
本研究通过文献计量学,从 2011 年至 2022 年在 Web of Science 核心合集数据库中确定了人工智能在废水处理中的应用文献,以总结成果并捕捉科技进步。发表的论文数量呈上升趋势,特别是 2018 年后发表的论文数量急剧增加,中国在此方面的贡献最大,其次是美国、伊朗和印度。德黑兰大学发表的论文数量最多,《WATER》是发表论文最多的期刊,而 Nasr M 发表的文章最多。合作网络主要通过欧洲国家、中国和美国之间的合作发展起来。发展中国家的遥感技术需要进一步与水质监测计划相结合。值得注意的是,人工神经网络是近年来的研究热点。通过关键词聚类分析,“机器学习”和“深度学习”是自 2019 年以来出现的热门关键词。神经网络在预测难降解废水处理效果方面的应用是未来的研究趋势。深度学习的快速发展为通过图像识别构建自动化管道缺陷检测系统提供了机会。