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Ding Y, Sun Q, Lin Y, Ping Q, Peng N, Wang L, Li Y. Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction. WATER RESEARCH 2024; 253:121267. [PMID: 38350192 DOI: 10.1016/j.watres.2024.121267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/30/2024] [Accepted: 02/04/2024] [Indexed: 02/15/2024]
Abstract
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 in (waste)water disinfection and reducing undesirable risks caused by disinfection processes.
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Affiliation(s)
- Yizhe Ding
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qiya Sun
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Yuqian Lin
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Qian Ping
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
| | - Nuo Peng
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China
| | - Lin Wang
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China.
| | - Yongmei Li
- State Key Laboratory of Pollution Control and Resource Reuse, Key Laboratory of Yangtze River Water Environment, College of Environmental Science and Engineering, Tongji University, Shanghai 200092, PR China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai 200092, PR China
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Foschi J, Turolla A, Antonelli M. Artificial neural network modeling of full-scale UV disinfection for process control aimed at wastewater reuse. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2021; 300:113790. [PMID: 34649313 DOI: 10.1016/j.jenvman.2021.113790] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 09/08/2021] [Accepted: 09/18/2021] [Indexed: 06/13/2023]
Abstract
Accurate modeling of wastewater ultraviolet disinfection is fundamental as support for process optimization and control. Detailed modeling of hydrodynamics and fluence rate via computational fluid dynamics, coupled to laboratory studies of inactivation kinetics, are usually the preferred approach for UV disinfection modeling. Despite this approach often provides accurate predictive performance, it requires significantly high computational time, making it unfeasible for real-time process control. In this study, to enable an effective process control, black-box regression models were assessed as a modeling alternative for UV disinfection, synthesizing hydrodynamics, fluence rate and inactivation kinetics. UV disinfection of a full-scale wastewater treatment plant in Italy was monitored for 10 months, measuring influent and effluent E. coli concentration, turbidity, absorbance at 254 nm, temperature and flow rate at different UV doses. Considering the usually observed distribution of effluent E. coli concentration and the zero inflation of the collected dataset, Poisson, zero-inflated Poisson and Hurdle generalized linear models were tested, as well as two-part models coupling a classifier describing the E. coli zero-count events and a regressor estimating the magnitude of E. coli concentrations in positive-count events. The two-part artificial neural network model showed the best predictive performance, being able of both describing nonlinearities and handling the high proportion of null values in the dataset. The deployment of this model to control ultraviolet disinfection was simulated, estimating a plausible 63% energy saving.
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Affiliation(s)
- Jacopo Foschi
- Politecnico di Milano, Department of Civil and Environmental Engineering (DICA), Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
| | - Andrea Turolla
- Politecnico di Milano, Department of Civil and Environmental Engineering (DICA), Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
| | - Manuela Antonelli
- Politecnico di Milano, Department of Civil and Environmental Engineering (DICA), Piazza Leonardo da Vinci 32, 20133, Milano, Italy.
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Pássaro ACM, Mozetic TM, Schmitz JE, da Silva IJ, Martins TD, Bresolin ITL. Human Immunoglobulin G Adsorption in Epoxy Chitosan/Alginate Adsorbents: Evaluation of Isotherms by Artificial Neural Networks. CHEMICAL PRODUCT AND PROCESS MODELING 2019. [DOI: 10.1515/cppm-2019-0077] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
This work aimed to evaluate the interaction of human IgG in non-conventional adsorbents based on chitosan and alginate in the absence and presence of Reactive Green, Reactive Blue and Cibacron Blue immobilized as ligands. The adsorption was evaluated at 277, 288, 298 and 310 K using sodium phosphate buffer, pH 7.6, at 25 mmol L−1. The highest adsorption capacity was observed in the experiments performed with no immobilized dye, although all showed adsorption capacity higher than 120 mg g−1. Data modeling was done using Langmuir, Langmuir-Freundlich and Temkin classical nonlinear models, and artificial neural networks (ANN) for comparison. According to the parameters obtained, a possible adsorption in multilayers was observed due to protein-adsorbent and protein-protein interactions, concluding that IgG adsorption process is favorable and spontaneous. Using an ANN structure with 3 hidden neurons (single hidden layer), the MSE (RMSE) for training, test and validation were 13.698 (3.701), 11.206 (3.347) and 7.632 (2.763), respectively, achieving correlation coefficients of 0.999 in all steps. ANN modeling proved to be effective in predicting the adsorption isotherms in addition to overcoming the difficulties caused by experimental errors and/or arising from adsorption phenomenology.
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Design and optimization of open-channel water ultraviolet disinfection reactor. CHEMICAL PAPERS 2019. [DOI: 10.1007/s11696-019-00694-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Patil-Shinde V, Saha S, Sharma BK, Tambe SS, Kulkarni BD. High Ash Char Gasification in Thermo-Gravimetric Analyzer and Prediction of Gasification Performance Parameters Using Computational Intelligence Formalisms. CHEM ENG COMMUN 2016. [DOI: 10.1080/00986445.2015.1135795] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Sultan T, Ahmad Z, Cho J. Optimization of lamp arrangement in a closed-conduit UV reactor based on a genetic algorithm. WATER SCIENCE AND TECHNOLOGY : A JOURNAL OF THE INTERNATIONAL ASSOCIATION ON WATER POLLUTION RESEARCH 2016; 73:2526-2543. [PMID: 27191576 DOI: 10.2166/wst.2016.119] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
The choice for the arrangement of the UV lamps in a closed-conduit ultraviolet (CCUV) reactor significantly affects the performance. However, a systematic methodology for the optimal lamp arrangement within the chamber of the CCUV reactor is not well established in the literature. In this research work, we propose a viable systematic methodology for the lamp arrangement based on a genetic algorithm (GA). In addition, we analyze the impacts of the diameter, angle, and symmetry of the lamp arrangement on the reduction equivalent dose (RED). The results are compared based on the simulated RED values and evaluated using the computational fluid dynamics simulations software ANSYS FLUENT. The fluence rate was calculated using commercial software UVCalc3D, and the GA-based lamp arrangement optimization was achieved using MATLAB. The simulation results provide detailed information about the GA-based methodology for the lamp arrangement, the pathogen transport, and the simulated RED values. A significant increase in the RED values was achieved by using the GA-based lamp arrangement methodology. This increase in RED value was highest for the asymmetric lamp arrangement within the chamber of the CCUV reactor. These results demonstrate that the proposed GA-based methodology for symmetric and asymmetric lamp arrangement provides a viable technical solution to the design and optimization of the CCUV reactor.
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Affiliation(s)
- Tipu Sultan
- Department of Mechanical Engineering, Hanyang University, Haengdang-dong, Sungdong-gu, Seoul 133-791, Republic of Korea E-mail:
| | - Zeshan Ahmad
- Department of Mechanical Engineering, School of Engineering (SEN), University of Management & Technology, Lahore, Pakistan
| | - Jinsoo Cho
- Department of Mechanical Engineering, Hanyang University, Haengdang-dong, Sungdong-gu, Seoul 133-791, Republic of Korea E-mail:
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