Novel approach for AI-based N 2 O emission reduction in biological wastewater treatment relying on genetic algorithms and neural networks.
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| Názov: | Novel approach for AI-based N |
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| Autori: | Freyschmidt A; Institute of Sanitary Engineering and Waste Management, Leibniz University Hannover, Welfengarten 1, 30167 Hannover, Germany E-mail: freyschmidt@isah.uni-hannover.de., Köster S; Institute of Sanitary Engineering and Waste Management, Leibniz University Hannover, Welfengarten 1, 30167 Hannover, Germany. |
| Zdroj: | Water science and technology : a journal of the International Association on Water Pollution Research [Water Sci Technol] 2025 May; Vol. 91 (10), pp. 1172-1184. Date of Electronic Publication: 2025 May 06. |
| Spôsob vydávania: | Journal Article |
| Jazyk: | English |
| Informácie o časopise: | Publisher: IWA Publishing Country of Publication: England NLM ID: 9879497 Publication Model: Print-Electronic Cited Medium: Print ISSN: 0273-1223 (Print) Linking ISSN: 02731223 NLM ISO Abbreviation: Water Sci Technol Subsets: MEDLINE |
| Imprint Name(s): | Publication: <1998->: London : IWA Publishing Original Publication: Oxford ; New York : Pergamon Press, 1981- |
| Výrazy zo slovníka MeSH: | Neural Networks, Computer* , Algorithms* , Wastewater*/chemistry , Waste Disposal, Fluid*/methods , Artificial Intelligence* , Nitrous Oxide* , Water Purification*/methods, Genetic Algorithms |
| Abstrakt: | The potential of measurement-based control strategies for achieving lower N (© 2025 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).) |
| Competing Interests: | The authors declare there is no conflict. |
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(2020) Mitigating nitrous oxide emissions at a full-scale wastewater treatment plant, Water Research, 185, 116196. doi:10.1016/j.watres.2020.116196. (PMID: 32738601) Freyschmidt A. & Beier M. (2022) Model-based identification and testing of appropriate strategies to minimize N Hwangbo S., Al R., Chen X. & Sin G. (2021) Integrated model for understanding N Icha, P., Lauf, T. & Kuhs, G. (2022) Entwicklung der spezifischen Treibhausgas-Emissionen des deutschen Strommix in den Jahren 1990–2021 [Development of the specific greenhouse gas emissions of the German energy mixture in the years 1990–2021] . Dessau-Roßlau, Germany: Umweltbundesamt. ISSN 1862-4359. IPCC (2021) IPCC 6th Assessment Report, The Physical Science Basis. Cambridge, UK and New York, NY, USA: Intergovernmental Panel on Climate Change. Kuokkanen A., Blomberg K., Mikola A. & Heinonen M. 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| Grant Information: | Volkswagen Foundation; Niedersächsisches Ministerium für Wissenschaft und Kultur; Bundesministerium für Bildung und Forschung |
| Contributed Indexing: | Keywords: N |
| Substance Nomenclature: | 0 (Wastewater) K50XQU1029 (Nitrous Oxide) |
| Entry Date(s): | Date Created: 20250531 Date Completed: 20250531 Latest Revision: 20250531 |
| Update Code: | 20250601 |
| DOI: | 10.2166/wst.2025.060 |
| PMID: | 40448459 |
| Databáza: | MEDLINE |
Nájsť tento článok vo Web of Science | Abstrakt: | The potential of measurement-based control strategies for achieving lower N <subscript>2</subscript> O emissions in biological wastewater treatment is limited due to strong temporal variations in N <subscript>2</subscript> O emissions and a lack of measurement data regarding influencing parameters. To address this issue, a novel artificial intelligence (AI)-based process optimization method for minimizing N <subscript>2</subscript> O emissions was developed, relying on a genetic algorithm to automatically determine the control settings associated with minimum N <subscript>2</subscript> O emissions for an individual operating situation. The genetic algorithm employs a validated prediction model to evaluate the effect of individual control parameter sets on N <subscript>2</subscript> O emissions and other operating targets. For this purpose, neural networks were trained using data generated with a mechanistic model. This approach is beneficial in practical applications as prediction networks could be successfully trained even if only limited data is available. The developed method also includes a classification algorithm to check the reliability of the AI-suggested control strategy. Two modeling studies confirm that the practical application of the developed methodology holds the potential for a considerable reduction in emissions (43% or 1,588 t CO <subscript>2</subscript> e/a) while still achieving the required effluent quality. Operational settings are identified in less than 2 minutes so that the approach can be applied on a large scale.<br /> (© 2025 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).) |
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| ISSN: | 0273-1223 |
| DOI: | 10.2166/wst.2025.060 |