Systematic Development of a Machine Learning-Based Asset Management Tool for Wastewater Pipeline NetworksClick to copy article linkArticle link copied!
- Jake StengelJake StengelDepartment of Chemical Engineering, Rowan University, Glassboro, New Jersey 08028-1700, United StatesMore by Jake Stengel
- Emmanuel AboagyeEmmanuel AboagyeDepartment of Chemical Engineering, Rowan University, Glassboro, New Jersey 08028-1700, United StatesMore by Emmanuel Aboagye
- Phuong LePhuong LeDepartment of Chemical Engineering, Rowan University, Glassboro, New Jersey 08028-1700, United StatesMore by Phuong Le
- Matt DeNafoMatt DeNafoAtlantic County Utility Authorities (ACUA), Atlantic City, New Jersey 08401, United StatesMore by Matt DeNafo
- Dylan SnyderDylan SnyderDepartment of Chemical Engineering, Rowan University, Glassboro, New Jersey 08028-1700, United StatesMore by Dylan Snyder
- Nathanial NelsonNathanial NelsonDepartment of Chemical Engineering, Rowan University, Glassboro, New Jersey 08028-1700, United StatesMore by Nathanial Nelson
- Kirti Yenkie*Kirti Yenkie*Email: [email protected]Department of Chemical Engineering, Rowan University, Glassboro, New Jersey 08028-1700, United StatesMore by Kirti Yenkie
Abstract
Utility companies face significant challenges in managing wastewater distribution networks (WWDN), where continuous service delivery is crucial. The American Society of Civil Engineers (ASCE) rated the U.S. wastewater treatment infrastructure as inadequate, assigning it a D+ grade due to its high risk of failure. Such failures can lead to severe economic and environmental damage, with untreated waste contaminating ecosystems and causing costly cleanups. Traditionally, the industry has relied on reactive, subjective asset management, addressing issues only after failures occur. This reactive approach often results in unexpected expenses and strains operational budgets. To address these challenges, we propose a proactive, data-driven asset management framework for the wastewater industry. Our strategy aims to reduce unforeseen costs by minimizing the likelihood of asset failure, environmental risks, and financial losses. By leveraging machine learning, specifically random forest classification, and analyzing historical data, we developed a predictive tool in Python. This tool identifies high-risk assets, enabling prioritized maintenance actions, ultimately mitigating potential environmental impacts and associated costs.
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*Disclaimer
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License Summary*
You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:
Creative Commons (CC): This is a Creative Commons license.
Attribution (BY): Credit must be given to the creator.
*Disclaimer
This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials.
License Summary*
You are free to share(copy and redistribute) this article in any medium or format and to adapt(remix, transform, and build upon) the material for any purpose, even commercially within the parameters below:
Creative Commons (CC): This is a Creative Commons license.
Attribution (BY): Credit must be given to the creator.
*Disclaimer
This summary highlights only some of the key features and terms of the actual license. It is not a license and has no legal value. Carefully review the actual license before using these materials.
Note Added after ASAP Publication
Due to a production error, this paper was published ASAP on November 18, 2024 with the wrong Supporting Information file. The corrected version was reposted on November 19, 2024.
Synopsis
This research presents a proactive asset management framework using machine learning to enhance the reliability and reduce costs in wastewater treatment networks.
1. Introduction
Figure 1
Figure 1. Funding gap representing the capital investment needed for the WWDN infrastructure across the United States reported by the ASCE.
2. Materials and Methods
2.1. Conventional Asset Management Strategy
Figure 2
Figure 2. Development process of an asset management plan.
2.2. Overall Preventive Measurement Number (OPMN) Metric
2.3. Analysis Framework
Figure 3
Figure 3. Framework for the development of a generalized asset management tool for use in utility authorities.
2.3.1. Management of Data Input
Risk Probability (RP) | Failure Impact (FI) | Data Type |
---|---|---|
Flow Types (−) | Flow Types (−) | Categorical |
Pipe Size – Diameter (in) | Pipe Size – Diameter (in) | Numerical |
Years since last Inspection (yr) | Years since last Inspection (yr) | Numerical |
Population density (people/mi2) | Population density (people/mi2) | Numerical |
Pipe Material Type (−) | Pipeline Placement in WWDN (−) | Categorical |
Pipe Segment Length (ft) | Flow Rates (MGD) | Numerical |
Original Installation Year (yr) | Numerical | |
Remaining Life (yr) | Numerical |
Category | RP | FI |
---|---|---|
1 | Little to no chance of asset failure in the near future | Little to no impact on the surrounding environment in the event of asset failure |
2 | Minor chance of asset failure in the near future | Minor impact on the surrounding environment in the event of asset failure |
3 | Moderate chance of asset failure in near future | Moderate impact on the surrounding environment in the event of asset failure |
4 | Major chance of asset failure in near future | Major impact on the surrounding environment in the event of asset failure |
5 | High chance of asset failure in near future | Extreme impact on the surrounding environment in the event of asset failure |
2.3.2. Asset Management ML Model Development
2.3.3. Setup and Computational Workflow
Figure 4
Figure 4. Programming and software packages used for application development.
3. Results and Discussions
3.1. Optimal Hyperparameters and Cross-Validation
Random Forest Classification (RFC) | XGBoost Classification (XGBC) | |||
---|---|---|---|---|
Hyperparameter | RP | FI | RP | FI |
Number of trees | 310 | 110 | 10 | 110 |
Depth of trees | 3 | 19 | 1 | 4 |
Cross-validation score (%) | 93.4 | 91.3 | 92.3 | 92.9 |
3.2. Model Evaluation
RFC | XGBC | |||
---|---|---|---|---|
Metric | RP | FI | RP | FI |
F1 score (%) | 90.2 | 94.7 | 92.0 | 95.9 |
Precision score (%) | 88.8 | 94.3 | 91.3 | 95.3 |
Accuracy score (%) | 91.8 | 95.3 | 92.9 | 96.5 |
3.3. Model Feature Importance
Figure 5
Figure 5. Breakdown of feature importance: (a) RP using the RFC model, (b) RP using the XGBC model, (c) FI using the RFC model, and (d) FI using the XGBC model.
Figure 6
Figure 6. Breakdown of the feature importance for the OPMN using eq 1). (a) Mean feature importance using RFC model. (b) Mean feature importance using XGBC model.
3.4. Asset Management Software Tool
Figure 7
Figure 7. GUI tab showing high-risk pipelines.
Figure 8
Figure 8. GUI tab showing detailed information about the individual asset and its corresponding OPMN.
Figure 9
Figure 9. GUI tab showing the feature importance for the base and updated model.
4. Conclusions
Data Availability
The data and machine learning model are available from the kmygroup GitHub Repository at https://github.com/kmygroup/Wastewater-Asset-Management-Project. The repository includes Python source code, and MySQL source code.
Supporting Information
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsestwater.4c00608.
Description of each Python file in the GitHub Repository (PDF)
Terms & Conditions
Most electronic Supporting Information files are available without a subscription to ACS Web Editions. Such files may be downloaded by article for research use (if there is a public use license linked to the relevant article, that license may permit other uses). Permission may be obtained from ACS for other uses through requests via the RightsLink permission system: http://pubs.acs.org/page/copyright/permissions.html.
Acknowledgments
The authors would like to thank the Department of Chemical Engineering at Rowan University for their resources and support. This work was funded and supported by the Atlantic County Utilities Authority.
References
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Abstract
Figure 1
Figure 1. Funding gap representing the capital investment needed for the WWDN infrastructure across the United States reported by the ASCE.
Figure 2
Figure 2. Development process of an asset management plan.
Figure 3
Figure 3. Framework for the development of a generalized asset management tool for use in utility authorities.
Figure 4
Figure 4. Programming and software packages used for application development.
Figure 5
Figure 5. Breakdown of feature importance: (a) RP using the RFC model, (b) RP using the XGBC model, (c) FI using the RFC model, and (d) FI using the XGBC model.
Figure 6
Figure 6. Breakdown of the feature importance for the OPMN using eq 1). (a) Mean feature importance using RFC model. (b) Mean feature importance using XGBC model.
Figure 7
Figure 7. GUI tab showing high-risk pipelines.
Figure 8
Figure 8. GUI tab showing detailed information about the individual asset and its corresponding OPMN.
Figure 9
Figure 9. GUI tab showing the feature importance for the base and updated model.
References
This article references 40 other publications.
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- 2Munim, Z. H.; Schramm, H.-J. The Impacts of Port Infrastructure and Logistics Performance on Economic Growth: The Mediating Role of Seaborne Trade. J. Shipp. Trade 2018, 3 (1), 1, DOI: 10.1186/s41072-018-0027-0There is no corresponding record for this reference.
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- 23Winkler, D.; Haltmeier, M.; Kleidorfer, M.; Rauch, W.; Tscheikner-Gratl, F. Pipe Failure Modelling for Water Distribution Networks Using Boosted Decision Trees. Struct. Infrastruct. Eng. 2018, 14 (10), 1402– 1411, DOI: 10.1080/15732479.2018.1443145There is no corresponding record for this reference.
- 24Fontecha, J. E.; Agarwal, P.; Torres, M. N.; Mukherjee, S.; Walteros, J. L.; Rodríguez, J. P. A Two-Stage Data-Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms. Risk Anal. 2021, 41 (12), 2356– 2391, DOI: 10.1111/risa.13742There is no corresponding record for this reference.
- 25Zhang, Y. New Advances in Machine Learning; BoD – Books on Demand, 2010.There is no corresponding record for this reference.
- 26Hammond, P.; Suttie, M.; Lewis, V. T.; Smith, A. P.; Singer, A. C. Detection of Untreated Sewage Discharges to Watercourses Using Machine Learning. Npj Clean Water 2021, 4 (1), 1– 10, DOI: 10.1038/s41545-021-00108-3There is no corresponding record for this reference.
- 27Torregrossa, D.; Leopold, U.; Hernández-Sancho, F.; Hansen, J. Machine Learning for Energy Cost Modelling in Wastewater Treatment Plants. J. Environ. Manage. 2018, 223, 1061– 1067, DOI: 10.1016/j.jenvman.2018.06.09227https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB3c7ntVSgtg%253D%253D&md5=0898baee7b2dfadda656a4acf15e7b68Machine learning for energy cost modelling in wastewater treatment plantsTorregrossa Dario; Leopold Ulrich; Hernandez-Sancho Francesc; Hansen JoachimJournal of environmental management (2018), 223 (), 1061-1067 ISSN:.Understanding the energy cost structure of wastewater treatment plants is a relevant topic for plant managers due to the high energy costs and significant saving potentials. Currently, energy cost models are generally generated using logarithmic, exponential or linear functions that could produce not accurate results when the relationship between variables is highly complex and non-linear. In order to overcome this issue, this paper proposes a new methodology based on machine-learning algorithms that perform better with complex datasets. In this paper, machine learning was used to generate high-performing energy cost models for wastewater treatment plants, using a database of 317 wastewater treatment plants located in north-west Europe. The most important variables in energy cost modelling were identified and for the first time, the energy price was used as model parameter and its importance evaluated.
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- 36Bergstra, J.; Yamins, D.; Cox, D. Hyperopt: A Python Library for Optimizing the Hyperparameters of Machine Learning Algorithms; SciPy: Austin: TX, 2013; pp 13– 19. DOI: DOI: 10.25080/Majora-8b375195-003 .There is no corresponding record for this reference.
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- 39Niu, S.; Liu, Y.; Wang, J.; Song, H. A Decade Survey of Transfer Learning (2010–2020). IEEE Trans. Artif. Intell. 2020, 1 (2), 151– 166, DOI: 10.1109/TAI.2021.3054609There is no corresponding record for this reference.
- 40Hosna, A.; Merry, E.; Gyalmo, J.; Alom, Z.; Aung, Z.; Azim, M. A. Transfer Learning: A Friendly Introduction. J. Big Data 2022, 9 (1), 102, DOI: 10.1186/s40537-022-00652-w40https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BB28zmsVeksg%253D%253D&md5=931ff6695fb906766a22277e4b9f1353Transfer learning: a friendly introductionHosna Asmaul; Merry Ethel; Gyalmo Jigmey; Alom Zulfikar; Azim Mohammad Abdul; Aung ZeyarJournal of big data (2022), 9 (1), 102 ISSN:2196-1115.Infinite numbers of real-world applications use Machine Learning (ML) techniques to develop potentially the best data available for the users. Transfer learning (TL), one of the categories under ML, has received much attention from the research communities in the past few years. Traditional ML algorithms perform under the assumption that a model uses limited data distribution to train and test samples. These conventional methods predict target tasks undemanding and are applied to small data distribution. However, this issue conceivably is resolved using TL. TL is acknowledged for its connectivity among the additional testing and training samples resulting in faster output with efficient results. This paper contributes to the domain and scope of TL, citing situational use based on their periods and a few of its applications. The paper provides an in-depth focus on the techniques; Inductive TL, Transductive TL, Unsupervised TL, which consists of sample selection, and domain adaptation, followed by contributions and future directions.
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