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IBM Cognitive Technology Helps Aqualia to Reduce Costs and Save Resources in Wastewater Treatment

Author

Listed:
  • Alexander Zadorojniy

    (IBM Research, Haifa 3498825, Israel)

  • Segev Wasserkrug

    (IBM Research, Haifa 3498825, Israel)

  • Sergey Zeltyn

    (IBM Research, Haifa 3498825, Israel)

  • Vladimir Lipets

    (IBM Research, Haifa 3498825, Israel)

Abstract

This work addresses operational management optimization problems in wastewater treatment plants. We developed a novel technology that allows control of such plants, based on real-time sensor readings, with cloud computing at the front end and state-of-the-art operations research and data science algorithms at the back end. We used a constrained Markov decision process as the key optimization framework. We tested our technology in a one-year pilot at a plant in Lleida, Spain, operated by Aqualia, the world’s third-largest water company. The results showed a dramatic 13.5 percent general reduction in the plant’s electricity consumption, a 14 percent reduction in the amount of chemicals needed to remove phosphorus from the water, and a 17 percent reduction in sludge production. Moreover, results showed a significant improvement in total nitrogen removal, especially in low temperature conditions.

Suggested Citation

  • Alexander Zadorojniy & Segev Wasserkrug & Sergey Zeltyn & Vladimir Lipets, 2017. "IBM Cognitive Technology Helps Aqualia to Reduce Costs and Save Resources in Wastewater Treatment," Interfaces, INFORMS, vol. 47(5), pages 411-424, October.
    • Handle: RePEc:inm:orinte:v:47:y:2017:i:5:p:411-424
    DOI: 10.1287/inte.2017.0907
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    References listed on IDEAS

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    1. Alan S. Manne, 1960. "Linear Programming and Sequential Decisions," Management Science, INFORMS, vol. 6(3), pages 259-267, April.
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    Cited by:

    1. Haimonti Dutta, 2022. "A Consensus Algorithm for Linear Support Vector Machines," Management Science, INFORMS, vol. 68(5), pages 3703-3725, May.

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