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Modeling and optimization of a wastewater pumping system with data-mining methods

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  • Zhang, Zijun
  • Kusiak, Andrew
  • Zeng, Yaohui
  • Wei, Xiupeng

Abstract

In this paper, a data-driven framework for improving the performance of wastewater pumping systems has been developed by fusing knowledge including the data mining, mathematical modeling, and computational intelligence. Modeling pump system performance in terms of the energy consumption and pumped wastewater flow rate based on industrial data with neural networks is examined. A bi-objective optimization model incorporating data-driven components is formulated to minimize the energy consumption and maximize the pumped wastewater flow rate. An adaptive mechanism is developed to automatically determine weights associated with two objectives by considering the wet well level and influent flow rate. The optimization model is solved by an artificial immune network algorithm. A comparative analysis between the optimization results and the observed data is performed to demonstrate the improvement of the pumping system performance. Results indicate that saving energy while maintaining the pumping performance is potentially achievable with the proposed data-driven framework.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:164:y:2016:i:c:p:303-311
    DOI: 10.1016/j.apenergy.2015.11.061
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    Cited by:

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    7. 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).
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    9. Zhang, Chaobo & Xue, Xue & Zhao, Yang & Zhang, Xuejun & Li, Tingting, 2019. "An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. 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.
    11. 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.
    12. Levon Gevorkov & José Luis Domínguez-García & Lluis Trilla Romero, 2022. "Review on Solar Photovoltaic-Powered Pumping Systems," Energies, MDPI, vol. 16(1), pages 1-21, December.
    13. 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.
    14. 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.
    15. 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.
    16. Kan, Guangyuan & Zhang, Mengjie & Liang, Ke & Wang, Hao & Jiang, Yunzhong & Li, Jiren & Ding, Liuqian & He, Xiaoyan & Hong, Yang & Zuo, Depeng & Bao, Zhenxin & Li, Chaochao, 2018. "Improving water quantity simulation & forecasting to solve the energy-water-food nexus issue by using heterogeneous computing accelerated global optimization method," Applied Energy, Elsevier, vol. 210(C), pages 420-433.
    17. Oreste Fecarotta & Armando Carravetta & Maria Cristina Morani & Roberta Padulano, 2018. "Optimal Pump Scheduling for Urban Drainage under Variable Flow Conditions," Resources, MDPI, vol. 7(4), pages 1-20, November.
    18. 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).
    19. Panagiotis Karadimos & Leonidas Anthopoulos, 2023. "Machine Learning-Based Energy Consumption Estimation of Wastewater Treatment Plants in Greece," Energies, MDPI, vol. 16(21), pages 1-20, November.

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