Skip to main content

Performance Degradation Monitoring and Quantification: A Wastewater Treatment Plant Case Study

  • Chapter
  • First Online:
  • 5175 Accesses

Abstract

The performance of electronic and mechanic components used in any industrial process changes over time. The wear generated by uninterrupted usage and the external conditions increase the probability of suffering a failure and also make them less efficient, by reducing their performance and increasing the operational costs. To prevent these consequences, maintenance works are carried out periodically. In order to establish a predictive maintenance plan, it is necessary to have reliable analysis of the performance of these components. For this, a data-driven approach to quantify and monitor the performance degradation of this equipment is presented. This approach is tested over wastewater treatment plant equipment, the blowers from an aeration system and the pumps of two pumping systems. The results obtained with this approach had been validated by the plant managers.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Aizenchtadt, E., Ingman, D., Friedler, E.: Quality control of wastewater treatment: a new approach. Eur. J. Oper. Res. 189(2), 445–458 (2008)

    Article  Google Scholar 

  2. Ayesa, E., De la Sota, A., Grau, P., Sagarna, J., Salterain, A., Suescun, J.: Supervisory control strategies for the new WWTP of Galindo-Bilbao: the long run from the conceptual design to the full-scale experimental validation. Water Sci. Technol. 53(4–5), 193–201 (2006)

    Article  Google Scholar 

  3. Barán, B., von Lücken, C., Sotelo, A.: Multi-objective pump scheduling optimisation using evolutionary strategies. Adv. Eng. Softw. 36(1), 39–47 (2005)

    Article  Google Scholar 

  4. Berge, S., Lund, B., Ugarelli, R.: Condition monitoring for early failure detection. Frognerparken pumping station as case study. Proc. Eng. 70, 162–171 (2014)

    Google Scholar 

  5. Castellet, L., Molinos-Senante, M.: Efficiency assessment of wastewater treatment plants: a data envelopment analysis approach integrating technical, economic, and environmental issues. J. Environ. Manag. 167, 160–166 (2016)

    Article  Google Scholar 

  6. Cleveland, W.S.: Robust locally weighted regression and smoothing scatterplots. J. Am. Stat. Assoc. 74(368), 829–836 (1979)

    Article  MathSciNet  Google Scholar 

  7. Cleveland, W.S., Devlin, S.J.: Locally weighted regression: an approach to regression analysis by local fitting. J. Am. Stat. Assoc. 83(403), 596–610 (1988)

    Article  Google Scholar 

  8. Comas, J., Dzeroski, S., Sànchez-Marrè, M.: Applying Machine Learning Methods to Wastewater Treatment Plant Data. Universitat Politècnica de Catalunya, Spain (2000)

    Google Scholar 

  9. da Costa Bortoni, E., de Almeida, R.A., Viana, A.N.C.: Optimization of parallel variable-speed-driven centrifugal pumps operation. Energ. Effic. 1(3), 167–173 (2008)

    Article  Google Scholar 

  10. Di Lorenzo, M., Scott, K., Curtis, T.P., Katuri, K.P., Head, I.M.: Continuous feed microbial fuel cell using an air cathode and a disc anode stack for wastewater treatment. Energy Fuel 23(11), 5707–5716 (2009)

    Article  Google Scholar 

  11. DOE, U.: Improving pumping system performance: a sourcebook for industry. Prepared for the US Department of Energy, Motor Challenge Program by Lawrence Berkeley National Laboratory (LBNL) and Resource Dynamics Corporation (RDC) (1999)

    Google Scholar 

  12. Eurostat: Simplified energy balances - annual data. Technical report, European Commission (2017)

    Google Scholar 

  13. Falkner, H., Reeves, D.: Study on improving the energy efficiency of pumps. European Commission (2001)

    Google Scholar 

  14. Friedman, J.H.: Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991)

    Article  MathSciNet  Google Scholar 

  15. Friedman, J., Hastie, T., Tibshirani, R.: The Elements of Statistical Learning, vol. 1. Springer Series in Statistics. Springer, New York (2001)

    Google Scholar 

  16. Goldstein, R., Smith, W.: Water & Sustainability (Volume 4): US Electricity Consumption for Water Supply & Treatment-The Next Half Century. Electric Power Research Institute, Palo Alto (2002)

    Google Scholar 

  17. Gude, V.G.: Energy and water autarky of wastewater treatment and power generation systems. Renew. Sust. Energ. Rev. 45, 52–68 (2015)

    Article  Google Scholar 

  18. Hand, D.J., Mannila, H., Smyth, P.: Principles of Data Mining. MIT press, Cambridge (2001)

    Google Scholar 

  19. Henze, M., van Loosdrecht, M.C., Ekama, G.A., Brdjanovic, D.: Biological Wastewater Treatment. IWA publishing, London (2008)

    Google Scholar 

  20. Hernández-Sancho, F., Molinos-Senante, M., Sala-Garrido, R.: Energy efficiency in Spanish wastewater treatment plants: a non-radial DEA approach. Sci. Total Environ. 409(14), 2693–2699 (2011)

    Article  Google Scholar 

  21. Kohavi, R., et al.: A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI, vol. 14, pp. 1137–1145, Montreal, Canada (1995)

    Google Scholar 

  22. Kusiak, A., Zeng, Y., Zhang, Z.: Modeling and analysis of pumps in a wastewater treatment plant: a data-mining approach. Eng. Appl. Artif. Intell. 26(7), 1643–1651 (2013)

    Article  Google Scholar 

  23. Moles, C., Gutierrez, G., Alonso, A., Banga, J., et al.: Integrated process design and control via global optimization-a wastewater treatment plant case study. Chem. Eng. Res. Des. 81(5), 507–517 (2003)

    Article  Google Scholar 

  24. Ngai, E.W., Xiu, L., Chau, D.C.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)

    Article  Google Scholar 

  25. Palaniappan, S., Awang, R.: Intelligent heart disease prediction system using data mining techniques. In: IEEE/ACS International Conference on Computer Systems and Applications, 2008. AICCSA 2008, pp. 108–115. IEEE, Piscataway (2008)

    Google Scholar 

  26. R Core Team: R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2018). https://www.R-project.org

  27. RStudio Team: RStudio: Integrated Development Environment for R. RStudio, Inc., Boston (2015). http://www.rstudio.com/

  28. Shao, J.: Linear model selection by cross-validation. J. Am. Stat. Assoc. 88(422), 486–494 (1993)

    Article  MathSciNet  Google Scholar 

  29. Stasyshan, R.: How inlet conditions impact centrifugal air compressor performance. https://www.airbestpractices.com/technology/air-compressors/how-inlet-conditions-impact-centrifugal-air-compressor-performance

  30. Thunberg, A., Sundin, A., Carlsson, B.: Energy optimization of the aeration process at Kappala wastewater treatment plant. In: 10th IWA Conference on Instrumentation, Control & Automation, pp. 14–17 (2009)

    Google Scholar 

  31. Torregrossa, D., Schutz, G., Cornelissen, A., Hernández-Sancho, F., Hansen, J.: Energy saving in WWTP: daily benchmarking under uncertainty and data availability limitations. Environ. Res. 148, 330–337 (2016)

    Article  Google Scholar 

  32. Torregrossa, D., Hansen, J., Hernández-Sancho, F., Cornelissen, A., Schutz, G., Leopold, U.: A data-driven methodology to support pump performance analysis and energy efficiency optimization in waste water treatment plants. Appl. Energy 208, 1430–1440 (2017)

    Article  Google Scholar 

  33. Torregrossa, D., Hansen, J., Hernández-Sancho, F., Cornelissen, A., Schutz, G., Leopold, U.: Pump efficiency analysis of waste water treatment plants: a data mining approach using signal decomposition for decision making. In: International Conference on Computational Science and Its Applications, pp. 744–752. Springer, Cham (2017)

    Chapter  Google Scholar 

  34. WEF, W.E.F.: Manual of practice (mop) no. 32: energy conservation in water and wastewater facilities. Prepared by the Energy Conservation in Water and Wastewater Treatment Facilities Task Force of the Water Environment Federation (2009)

    Google Scholar 

  35. Zeng, Y., Zhang, Z., Kusiak, A., Tang, F., Wei, X.: Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm. Stochastic Environ. Res. Risk Assess. 30(4), 1263–1275 (2016)

    Article  Google Scholar 

  36. Zhang, Z., Kusiak, A.: Models for optimization of energy consumption of pumps in a wastewater processing plant. J. Energy Eng. 137(4), 159–168 (2011)

    Article  Google Scholar 

  37. Zhang, Z., Zeng, Y., Kusiak, A.: Minimizing pump energy in a wastewater processing plant. Energy 47(1), 505–514 (2012)

    Article  Google Scholar 

  38. Zhang, Z., Kusiak, A., Zeng, Y., Wei, X.: Modeling and optimization of a wastewater pumping system with data-mining methods. Appl. Energy 164, 303–311 (2016)

    Article  Google Scholar 

Download references

Acknowledgements

This work has been developed by the Intelligent Systems for Industrial Systems group supported by the Department of Education, Language policy and Culture of the Basque Government. It has been partially funded by the Basque Government.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Iñigo Lecuona .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Lecuona, I., Basagoiti, R., Urchegui, G., Eciolaza, L., Zurutuza, U., Craamer, P. (2019). Performance Degradation Monitoring and Quantification: A Wastewater Treatment Plant Case Study. In: Lughofer, E., Sayed-Mouchaweh, M. (eds) Predictive Maintenance in Dynamic Systems. Springer, Cham. https://doi.org/10.1007/978-3-030-05645-2_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-05645-2_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05644-5

  • Online ISBN: 978-3-030-05645-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics