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Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm

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Abstract

Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven components and pump operational constraints is developed to minimize the energy consumption of the pumping system while maintaining the required pumping workload. A greedy electromagnetism-like (GEM) algorithm is designed to solve the MINLP model for optimized operational schedules and pump speeds. Three computational cases are studied to demonstrate the effectiveness of the proposed data-driven modeling and GEM algorithm. The computational results show that significant energy saving can be obtained.

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Acknowledgments

This research was supported by funding from the Iowa Energy Center, Grant No. 10-1.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, 3131 Seamans Center, The University of Iowa, Iowa City, IA, 52242–1527, USA

    Yaohui Zeng, Andrew Kusiak, Fan Tang & Xiupeng Wei

  2. Department of Systems Engineering and Engineering Management, City University of Hong Kong, P6600, 6/F, Academic 1, Kowloon Tong, Hong Kong

    Zijun Zhang

Authors
  1. Yaohui Zeng
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  2. Zijun Zhang
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  3. Andrew Kusiak
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  4. Fan Tang
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  5. Xiupeng Wei
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Corresponding author

Correspondence to Xiupeng Wei.

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Appendix

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Zeng, Y., Zhang, Z., Kusiak, A. et al. Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm. Stoch Environ Res Risk Assess 30, 1263–1275 (2016). https://doi.org/10.1007/s00477-015-1115-4

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