IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v208y2017icp1430-1440.html
   My bibliography  Save this article

A data-driven methodology to support pump performance analysis and energy efficiency optimization in Waste Water Treatment Plants

Author

Listed:
  • Torregrossa, Dario
  • Hansen, Joachim
  • Hernández-Sancho, Francesc
  • Cornelissen, Alex
  • Schutz, Georges
  • Leopold, Ulrich

Abstract

Studies and publications from the past ten years demonstrate that generally the energy efficiency of Waste Water Treatment Plants (WWTPs) is unsatisfactory. In this domain, efficient pump energy management can generate economic and environmental benefits. Although the availability of on-line sensors can provide high-frequency information about pump systems, at best, energy assessment is carried out a few times a year using aggregated data. Consequently, pump inefficiencies are normally detected late and the comprehension of pump system dynamics is often not satisfactory. In this paper, a data-driven methodology to support the daily energy decision-making is presented. This innovative approach, based on fuzzy logic, supports plant managers with detailed information about pump performance, and provides case-based suggestions to reduce the pump system energy consumption and extend pump life spans. A case study, performed on a WWTP in Germany, shows that it is possible to identify energy inefficiencies and case-based solutions to reduce the pump energy consumption by 18.5%.

Suggested Citation

  • Torregrossa, Dario & Hansen, Joachim & Hernández-Sancho, Francesc & Cornelissen, Alex & Schutz, Georges & Leopold, Ulrich, 2017. "A data-driven methodology to support pump performance analysis and energy efficiency optimization in Waste Water Treatment Plants," Applied Energy, Elsevier, vol. 208(C), pages 1430-1440.
  • Handle: RePEc:eee:appene:v:208:y:2017:i:c:p:1430-1440
    DOI: 10.1016/j.apenergy.2017.09.012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917312928
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2017.09.012?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhang, Zijun & Zeng, Yaohui & Kusiak, Andrew, 2012. "Minimizing pump energy in a wastewater processing plant," Energy, Elsevier, vol. 47(1), pages 505-514.
    2. Panepinto, Deborah & Fiore, Silvia & Zappone, Mariantonia & Genon, Giuseppe & Meucci, Lorenza, 2016. "Evaluation of the energy efficiency of a large wastewater treatment plant in Italy," Applied Energy, Elsevier, vol. 161(C), pages 404-411.
    3. Arun Shankar, Vishnu Kalaiselvan & Umashankar, Subramaniam & Paramasivam, Shanmugam & Hanigovszki, Norbert, 2016. "A comprehensive review on energy efficiency enhancement initiatives in centrifugal pumping system," Applied Energy, Elsevier, vol. 181(C), pages 495-513.
    4. DeBenedictis, A. & Haley, B. & Woo, C.K. & Cutter, E., 2013. "Operational energy-efficiency improvement of municipal water pumping in California," Energy, Elsevier, vol. 53(C), pages 237-243.
    5. Zhang, Zijun & Kusiak, Andrew & Zeng, Yaohui & Wei, Xiupeng, 2016. "Modeling and optimization of a wastewater pumping system with data-mining methods," Applied Energy, Elsevier, vol. 164(C), pages 303-311.
    6. Zhuan, Xiangtao & Xia, Xiaohua, 2013. "Optimal operation scheduling of a pumping station with multiple pumps," Applied Energy, Elsevier, vol. 104(C), pages 250-257.
    7. Olszewski, Pawel, 2016. "Genetic optimization and experimental verification of complex parallel pumping station with centrifugal pumps," Applied Energy, Elsevier, vol. 178(C), pages 527-539.
    8. Wang, Chuan & Shi, Weidong & Wang, Xikun & Jiang, Xiaoping & Yang, Yang & Li, Wei & Zhou, Ling, 2017. "Optimal design of multistage centrifugal pump based on the combined energy loss model and computational fluid dynamics," Applied Energy, Elsevier, vol. 187(C), pages 10-26.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Olszewski, Pawel & Arafeh, Jamal, 2018. "Parametric analysis of pumping station with parallel-configured centrifugal pumps towards self-learning applications," Applied Energy, Elsevier, vol. 231(C), pages 1146-1158.
    2. Filipe, Jorge & Bessa, Ricardo J. & Reis, Marisa & Alves, Rita & Póvoa, Pedro, 2019. "Data-driven predictive energy optimization in a wastewater pumping station," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    3. Wang, Zhiyuan & Qian, Zhongdong & Lu, Jie & Wu, Pengfei, 2019. "Effects of flow rate and rotational speed on pressure fluctuations in a double-suction centrifugal pump," Energy, Elsevier, vol. 170(C), pages 212-227.
    4. Arun Shankar, Vishnu Kalaiselvan & Umashankar, Subramaniam & Paramasivam, Shanmugam & Hanigovszki, Norbert, 2016. "A comprehensive review on energy efficiency enhancement initiatives in centrifugal pumping system," Applied Energy, Elsevier, vol. 181(C), pages 495-513.
    5. Xiaoli Feng & Baoyun Qiu & Yongxing Wang, 2020. "Optimizing Parallel Pumping Station Operations in an Open-Channel Water Transfer System Using an Efficient Hybrid Algorithm," Energies, MDPI, vol. 13(18), pages 1-19, September.
    6. Kirchem, Dana & Lynch, Muireann Á. & Bertsch, Valentin & Casey, Eoin, 2020. "Modelling demand response with process models and energy systems models: Potential applications for wastewater treatment within the energy-water nexus," Applied Energy, Elsevier, vol. 260(C).
    7. Safarbek Oshurbekov & Vadim Kazakbaev & Vladimir Prakht & Vladimir Dmitrievskii, 2021. "Improving Reliability and Energy Efficiency of Three Parallel Pumps by Selecting Trade-Off Operating Points," Mathematics, MDPI, vol. 9(11), pages 1-19, June.
    8. Chen, Weisheng & Li, Yaojun & Liu, Zhuqing & Hong, Yiping, 2023. "Understanding of energy conversion and losses in a centrifugal pump impeller," Energy, Elsevier, vol. 263(PB).
    9. Leandro Alves Evangelista & Gustavo Meirelles & Bruno Brentan, 2023. "Computational Model of Water Distribution Network Life Cycle Deterioration," Sustainability, MDPI, vol. 15(19), pages 1-14, October.
    10. Liu, Mingzhe & Ooka, Ryozo & Choi, Wonjun & Ikeda, Shintaro, 2019. "Experimental and numerical investigation of energy saving potential of centralized and decentralized pumping systems," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    11. Vojtěch Zejda & Vítězslav Máša & Šárka Václavková & Pavel Skryja, 2020. "A Novel Check-List Strategy to Evaluate the Potential of Operational Improvements in Wastewater Treatment Plants," Energies, MDPI, vol. 13(19), pages 1-21, September.
    12. Zhang, Ning & Jiang, Junxian & Gao, Bo & Liu, Xiaokai & Ni, Dan, 2020. "Numerical analysis of the vortical structure and its unsteady evolution of a centrifugal pump," Renewable Energy, Elsevier, vol. 155(C), pages 748-760.
    13. Zhang, Ning & Jiang, Junxian & Gao, Bo & Liu, Xiaokai, 2020. "DDES analysis of unsteady flow evolution and pressure pulsation at off-design condition of a centrifugal pump," Renewable Energy, Elsevier, vol. 153(C), pages 193-204.
    14. Ning Zhang & Delin Li & Bo Gao & Dan Ni & Zhong Li, 2022. "Unsteady Pressure Pulsations in Pumps—A Review," Energies, MDPI, vol. 16(1), pages 1-30, December.
    15. Johnson, Hilary A. & Simon, Kevin P. & Slocum, Alexander H., 2021. "Data analytics and pump control in a wastewater treatment plant," Applied Energy, Elsevier, vol. 299(C).
    16. Kirchem, Dana & Lynch, Muireann Á & Casey, Eoin & Bertsch, Valentin, 2019. "Demand response within the energy-for-water-nexus: A review," Papers WP637, Economic and Social Research Institute (ESRI).
    17. Xuetao Wang & Qianchuan Zhao & Yifan Wang, 2020. "A Distributed Optimization Method for Energy Saving of Parallel-Connected Pumps in HVAC Systems," Energies, MDPI, vol. 13(15), pages 1-24, July.
    18. Wang, Chuan & Shi, Weidong & Wang, Xikun & Jiang, Xiaoping & Yang, Yang & Li, Wei & Zhou, Ling, 2017. "Optimal design of multistage centrifugal pump based on the combined energy loss model and computational fluid dynamics," Applied Energy, Elsevier, vol. 187(C), pages 10-26.
    19. Ma, Jiaze & Wang, Yufei & Feng, Xiao, 2017. "Energy recovery in cooling water system by hydro turbines," Energy, Elsevier, vol. 139(C), pages 329-340.
    20. Prince, & Hati, Ananda Shankar, 2021. "A comprehensive review of energy-efficiency of ventilation system using Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:208:y:2017:i:c:p:1430-1440. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.