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

Alexander Zadorojniy (), Segev Wasserkrug (), Sergey Zeltyn () and Vladimir Lipets ()
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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

Interfaces, 2017, vol. 47, issue 5, 411-424

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.

Keywords: MDP; constraints; WWTP; machine learning; IoT (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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