The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse
Abstract
Abstract Wastewater treatment is an important step for pollutant reduction and the promotion of water environment quality. The complexity of natural conditions, influent shock, and wastewater treatment technology result in uncertainty and variation in the wastewater treatment system. These uncertainties result in fluctuations in effluent water quality and operation costs, as well as the environmental risk of receiving waters. Artificial intelligence has become a powerful tool for minimizing the complexities and complications in wastewater treatment. In this study, we examine the literature from 1995 to 2019 to conduct a large-scale bibliometric analysis of trends in the application of artificial intelligence technology to wastewater treatment. Furthermore, we present a systematic review of four aspects of the application of artificial intelligence to wastewater treatment: technology, economy, management, and wastewater reuse. Finally, we provide perspectives on the potential future directions of new research frontiers in the utilization of artificial intelligence in wastewater treatment plants that simultaneously address pollutant removal, cost reduction, water reuse, and management challenges in complex practical applications.
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Cites
Loos, R., Carvalho, R., António, D. C., Comero, S., Locoro, G., Tavazzi, S., Paracchini, B., Ghiani, M., Lettieri, T., Blaha, L., et al. (2013) · Water Research · doi:10.1016/j.watres.2013.08.024 · Cited by 1,193
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Innovations and challenges in adsorption-based wastewater remediation: A comprehensive review
Satyam, S., & Patra, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e29573 · Cited by 788
Zhang, Z., Chen, Z., Zhang, J., Liu, Y., Chen, L., Yang, M., Osman, A. I., Farghali, M., Liu, E., Hassan, D., et al. (2024) · Science of The Total Environment · doi:10.1016/j.scitotenv.2024.172794 · Cited by 384
Yigitcanlar, T., Mehmood, R., & Corchado, J. M. (2021) · Sustainability · doi:10.3390/su13168952 · Cited by 319
Kurniawan, T. A., Dzarfan Othman, M. H., Hwang, G. H., & Gikas, P. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.131911 · Cited by 282
Metadata
- Authors
- Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., & Yang, Y.
- Venue
- Process Safety and Environmental Protection
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 133
- Pages
- 169-182
- ISSN
- 0263-8762 (ISSN-L)
- 0957-5820
- OpenAlex
- W2986617680
- Cited by
- 440
- References
- 1
- FWCI
- 8.58
- Type
- journal article
- Language
- en
- Open access
- closed
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- The paper
- Zhao, L., Dai, T., Qiao, Z., Sun, P., Hao, J., & Yang, Y. (2020). Application of artificial intelligence to wastewater treatment: A bibliometric analysis and systematic review of technology, economy, management, and wastewater reuse. Process Safety and Environmental Protection. https://doi.org/10.1016/j.psep.2019.11.014
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
