The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review
Abstract
Abstract Because of its robust autonomous learning and ability to address complex problems, artificial intelligence (AI) has increasingly demonstrated its potential to solve the challenges faced in drinking water treatment (DWT). AI technology provides technical support for the management and operation of DWT processes, which is more efficient than relying solely on human operations. AI-based data analysis and evolutionary learning mechanisms are capable of realizing water quality diagnosis, autonomous decision making and operation process optimization and have the potential to establish a universal process analysis and predictive model platform. This review briefly introduces AI technologies that are widely used in DWT. Moreover, this paper reviews in detail the mature applications and latest discoveries of AI and machine learning technologies in the fields of source water quality, coagulation/flocculation, disinfection and membrane filtration, including source water contaminant monitoring and identification, accurate and efficient prediction of coagulation dosage, analysis of the formation of disinfection by-products and advanced control of membrane fouling. Finally, the challenges facing AI technologies and the issues that need further study are discussed; these challenges can be briefly summarized as a) obtaining more effective characterization data to screen and identify targeted contaminants in the complex background with the assistance of AI technologies and b) establishing a macro intelligence model and decision scheme for entire drinking water treatment plants (DWTPs) to support the management of the water supply system.
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Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Classification and Regression by randomForest
Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594
Deep learning in neural networks: an overview
Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570
Object Detection With Deep Learning: A Review
Zhao, Z.-Q., Zheng, P., Xu, S.-T., & Wu, X. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2876865 · Cited by 5,545
Artificial neural networks: a tutorial
Jain, A. K., Mao, J., & Mohiuddin, K. M. (1996) · Computer · doi:10.1109/2.485891 · Cited by 4,946
Machine Learning Applications in Cancer Prognosis and Prediction
Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., & Fotiadis, D. I. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2014.11.005 · Cited by 3,338
Intelligent Manufacturing in the Context of Industry 4.0: A Review
Zhong, R. Y., Xu, X., Klotz, E., & Newman, S. T. (2017) · Engineering · doi:10.1016/j.eng.2017.05.015 · Cited by 2,913
Olden, J. D., & Jackson, D. A. (2002) · Ecological Modelling · doi:10.1016/s0304-3800(02)00064-9 · Cited by 1,223
Chen, K., Chen, H., Zhou, C., Huang, Y., Qi, X., Shen, R., Liu, F., Zuo, M., Zou, X., Wang, J., et al. (2020) · Water Research · doi:10.1016/j.watres.2019.115454 · Cited by 580
Al’Aref, S. J., Anchouche, K., Singh, G., Slomka, P. J., Kolli, K. K., Kumar, A., Pandey, M., Maliakal, G., van Rosendael, A. R., Beecy, A. N., et al. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehy404 · Cited by 534
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Digitalization to achieve sustainable development goals: Steps towards a Smart Green Planet
Mondejar, M. E., Avtar, R., Diaz, H. L. B., Dubey, R. K., Esteban, J., Gómez-Morales, A., Hallam, B., Mbungu, N. T., Okolo, C. C., Prasad, K. A., et al. (2021) · Science of The Total Environment · doi:10.1016/j.scitotenv.2021.148539 · Cited by 916
Machine learning in natural and engineered water systems
Huang, R., Ma, C., Ma, J., Huangfu, X., & He, Q. (2021) · Water Research · doi:10.1016/j.watres.2021.117666 · Cited by 319
Biological Wastewater Treatment
Theophilus Ogunwumi, O., Festus Adeniyi, A., Chinazor Angus, M., & Sunday Oche, O. (2024) · Wastewater Treatment and Sludge Management Systems - The Gutter-to-Good Approaches · doi:10.5772/intechopen.1004638 · Cited by 267
Karbassiyazdi, E., Fattahi, F., Yousefi, N., Tahmassebi, A., Taromi, A. A., Manzari, J. Z., Gandomi, A. H., Altaee, A., & Razmjou, A. (2022) · Environmental Research · doi:10.1016/j.envres.2022.114286 · Cited by 104
Artificial intelligence in food science and nutrition: A narrative review
Miyazawa, T., Hiratsuka, Y., Toda, M., Hatakeyama, N., Ozawa, H., Abe, C., Cheng, T.-Y., Matsushima, Y., Miyawaki, Y., Ashida, K., et al. (2022) · Nutrition Reviews · doi:10.1093/nutrit/nuac033 · Cited by 91
Metadata
- Authors
- Li, L., Rong, S., Wang, R., & Yu, S.
- Venue
- Chemical Engineering Journal
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 405
- Pages
- 126673
- ISSN
- 1385-8947 (ISSN-L)
- OpenAlex
- W3049235151
- Cited by
- 441
- References
- 64
- FWCI
- 12.16
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, L., Rong, S., Wang, R., & Yu, S. (2021). Recent advances in artificial intelligence and machine learning for nonlinear relationship analysis and process control in drinking water treatment: A review. Chemical Engineering Journal. https://doi.org/10.1016/j.cej.2020.126673
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