Name
Title | ||
|---|---|---|
IBM Cognitive Technology Helps Aqualia to Reduce Costs and Save Resources in Wastewater Treatment |
Abstract | ||
|---|---|---|
AbstractThis 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1287/inte.2017.0907 | Periodicals |
Keywords | Field | DocType |
MDP, constraints, WWTP, machine learning, IoT | Front and back ends,Sludge,IBM,Electricity,Markov decision process,Sewage treatment,Engineering,Optimization problem,Operations management,Cloud computing | Journal |
Volume | Issue | ISSN |
47 | 5 | 0092-2102 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alexander Zadorojniy | 1 | 9 | 2.74 |
| Segev Wasserkrug | 2 | 140 | 12.25 |
| Sergey Zeltyn | 3 | 174 | 15.09 |
| Vladimir Lipets | 4 | 12 | 1.97 |