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Decision making methodology for maintenance of return pumps in wastewater treatment plant using machine learning.

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Názov: Decision making methodology for maintenance of return pumps in wastewater treatment plant using machine learning.
Autori: Yang S; Department of Energy and Environmental Engineering, The Catholic University of Korea, 43 Jibong-ro, Bucheon-si, Gyeonggi-do, Republic of Korea. Electronic address: yangaao@catholic.ac.kr., Lee KH; Department of Medical Informatics, College of Medicine, The Catholic University of Korea, 222 Banpo-daero, Seocho-gu, Seoul, 06591, Republic of Korea. Electronic address: diappong@catholic.ac.kr., Eom J; Department of Energy and Environmental Engineering, The Catholic University of Korea, 43 Jibong-ro, Bucheon-si, Gyeonggi-do, Republic of Korea. Electronic address: ejy0927@catholic.ac.kr., Lee M; Department of Energy and Environmental Engineering, The Catholic University of Korea, 43 Jibong-ro, Bucheon-si, Gyeonggi-do, Republic of Korea. Electronic address: audwls2lee@catholic.ac.kr., Lee KH; Department of Energy and Environmental Engineering, The Catholic University of Korea, 43 Jibong-ro, Bucheon-si, Gyeonggi-do, Republic of Korea. Electronic address: diasyong@catholic.ac.kr.
Zdroj: The Science of the total environment [Sci Total Environ] 2025 Dec 01; Vol. 1006, pp. 180940. Date of Electronic Publication: 2025 Nov 14.
Spôsob vydávania: Journal Article
Jazyk: English
Informácie o časopise: Publisher: Elsevier Country of Publication: Netherlands NLM ID: 0330500 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-1026 (Electronic) Linking ISSN: 00489697 NLM ISO Abbreviation: Sci Total Environ Subsets: MEDLINE
Imprint Name(s): Original Publication: Amsterdam, Elsevier.
Výrazy zo slovníka MeSH: Machine Learning* , Waste Disposal, Fluid*/methods , Waste Disposal, Fluid*/instrumentation , Wastewater* , Decision Making*
Abstrakt: Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
The current maintenance systems for pumps in wastewater treatment plants operate reactively, focusing on simple reinforcement measures when faults are detected rather than applying mid- to long-term strategic management. A maintenance paradigm that accurately predicts the condition of machines offers the potential to reduce maintenance costs and improve facility operations through preemptive strategies. This study emphasizes decision-making for maintenance management by establishing a real-world monitoring system, extracting operational data, and going through processes, namely, determination, collection, analysis, and modeling. Thresholds for asset conditions are set, and indicators are established for assessment and grading. Based on this, in predictive maintenance, extracted data are used to measure parameters through machine learning techniques and to prediction future performance for condition monitoring. This study presents a methodology for proactive pump asset management and suggests key considerations in maintenance strategies. The study strongly suggests that preventive pump maintenance can significantly reduce the risk of prolonged wastewater treatment outages and environmental discharges.
(Copyright © 2025 Elsevier B.V. All rights reserved.)
Substance Nomenclature: 0 (Wastewater)
Entry Date(s): Date Created: 20251115 Date Completed: 20251127 Latest Revision: 20251127
Update Code: 20251128
DOI: 10.1016/j.scitotenv.2025.180940
PMID: 41240889
Databáza: MEDLINE
Popis
Abstrakt:Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.<br />The current maintenance systems for pumps in wastewater treatment plants operate reactively, focusing on simple reinforcement measures when faults are detected rather than applying mid- to long-term strategic management. A maintenance paradigm that accurately predicts the condition of machines offers the potential to reduce maintenance costs and improve facility operations through preemptive strategies. This study emphasizes decision-making for maintenance management by establishing a real-world monitoring system, extracting operational data, and going through processes, namely, determination, collection, analysis, and modeling. Thresholds for asset conditions are set, and indicators are established for assessment and grading. Based on this, in predictive maintenance, extracted data are used to measure parameters through machine learning techniques and to prediction future performance for condition monitoring. This study presents a methodology for proactive pump asset management and suggests key considerations in maintenance strategies. The study strongly suggests that preventive pump maintenance can significantly reduce the risk of prolonged wastewater treatment outages and environmental discharges.<br /> (Copyright © 2025 Elsevier B.V. All rights reserved.)
ISSN:1879-1026
DOI:10.1016/j.scitotenv.2025.180940