Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

被引：18

作者：

Ding, Yizhe ^{[1
]}

Sun, Qiya ^{[1
]}

Lin, Yuqian ^{[1
]}

Ping, Qian ^{[1
,2
]}

Peng, Nuo ^{[1
]}

Wang, Lin ^{[1
,2
]}

Li, Yongmei ^{[1
,2
]}

机构：

[1] Tongji Univ, Coll Environm Sci & Engn, Key Lab Yangtze River Water Environm, State Key Lab Pollut Control & Resource Reuse, Shanghai 200092, Peoples R China

[2] Shanghai Inst Pollut Control & Ecol Secur, Shanghai 200092, Peoples R China

来源：

WATER RESEARCH | 2024年 / 253卷

关键词：

Artificial intelligence; Water/wastewater disinfection; Disinfection by-products; Disinfection residuals; NEURAL-NETWORK MODELS; WATER DISTRIBUTION-SYSTEMS; MUNICIPAL WASTE-WATER; RIVER DELTA REGION; DRINKING-WATER; REGRESSION-MODELS; NDMA FORMATION; CHLORINE; OPTIMIZATION; FRAMEWORK;

D O I：

10.1016/j.watres.2024.121267

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

Water/wastewater ((waste)water) disinfection, as a critical process during drinking water or wastewater treatment, can simultaneously inactivate pathogens and remove emerging organic contaminants. Due to fluctuations of (waste)water quantity and quality during the disinfection process, conventional disinfection models cannot handle intricate nonlinear situations and provide immediate responses. Artificial intelligence (AI) techniques, which can capture complex variations and accurately predict/adjust outputs on time, exhibit excellent performance for (waste)water disinfection. In this review, AI application data within the disinfection domain were searched and analyzed using CiteSpace. Then, the application of AI in the (waste)water disinfection process was comprehensively reviewed, and in addition to conventional disinfection processes, novel disinfection processes were also examined. Then, the application of AI in disinfection by-products (DBPs) formation control and disinfection residues prediction was discussed, and unregulated DBPs were also examined. Current studies have suggested that among AI techniques, fuzzy logic-based neuro systems exhibit superior control performance in (waste)water disinfection, while single AI technology is insufficient to support their applications in full-scale (waste)water treatment plants. Thus, attention should be paid to the development of hybrid AI technologies, which can give full play to the characteristics of different AI technologies and achieve a more refined effectiveness. This review provides comprehensive information for an in-depth understanding of AI application (waste)water disinfection and reducing undesirable risks caused by disinfection processes.

引用

页数：19

共 137 条

[1] Modeling and prediction of trihalomethanes in the drinking water treatment plant of Thessaloniki, Greece [J].

Albanakis, C. ;

Tsanana, E. ;

Fragkaki, A. G. .

JOURNAL OF WATER PROCESS ENGINEERING, 2021, 43

[2] Regulated and emerging disinfection by-products in recycled waters [J].

Alexandrou, Lydon ;

Meehan, Barry J. ;

Jones, Oliver A. H. .

SCIENCE OF THE TOTAL ENVIRONMENT, 2018, 637 :1607-1616

[3] Statistical experimental design, least squares-support vector machine (LS-SVM) and artificial neural network (ANN) methods for modeling the facilitated adsorption of methylene blue dye [J].

Asfaram, A. ;

Ghaedi, M. ;

Azqhandi, M. H. Ahmadi ;

Goudarzi, A. ;

Dastkhoon, M. .

RSC ADVANCES, 2016, 6 (46) :40502-40516

[4] Modeling the degradation and disinfection of water pollutants by photocatalysts and composites: A critical review [J].

Ateia, Mohamed ;

Alalm, Mohamed Gar ;

Awfa, Dion ;

Johnson, Matthew S. ;

Yoshimura, Chihiro .

SCIENCE OF THE TOTAL ENVIRONMENT, 2020, 698

[5] Investigation of the effective factors on the mutagen X formation in drinking water by response surface methodology [J].

Bagheban, Mahtab ;

Baghdadi, Majid ;

Mohammadi, Ali ;

Roozbehnia, Parisa .

JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2019, 251

[6]

Bashayreh E, 2021, Ecological Engineering & Environmental Technology, V22, P109, DOI 10.12912/27197050/132088

[7] Fully Automated Unconstrained Analysis of High-Resolution Mass Spectrometry Data with Machine Learning [J].

Boiko, Daniil A. ;

Kozlov, Konstantin S. ;

V. Burykina, Julia ;

Ilyushenkova, Valentina V. ;

Ananikov, Valentine P. .

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY, 2022, 144 (32) :14590-14606

[8] Forecasting chlorine residuals in a water distribution system using a general regression neural network [J].

Bowden, Gavin J. ;

Nixon, John B. ;

Dandy, Graerne C. ;

Maier, Holger R. ;

Holmes, Mike .

MATHEMATICAL AND COMPUTER MODELLING, 2006, 44 (5-6) :469-484

[9] Method for establishing predictive models for total organic halogen based on piecewise interpolation and machine learning [J].

Bu, Yinan ;

Shi, Liangliang ;

Ma, Bin .

JOURNAL OF ENVIRONMENTAL CHEMICAL ENGINEERING, 2023, 11 (03)

[10] Robust evaluation of performance monitoring options for ozone disinfection in water recycling using Bayesian analysis [J].

Carvajal, Guido ;

Branch, Amos ;

Michel, Philipp ;

Sisson, Scott A. ;

Roser, David J. ;

Drewes, Joerg E. ;

Khan, Stuart J. .

WATER RESEARCH, 2017, 124 :605-617

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