2021 Fiscal Year Annual Research Report
Autonomous wastewater treatment: Monitoring and control of on-site wastewater treatment plants
| Project/Area Number |
20F20763
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| Research Institution | The University of Tokyo |
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Principal Investigator |
國吉 康夫 東京大学, 大学院情報理工学系研究科, 教授 (10333444)
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| Co-Investigator(Kenkyū-buntansha) |
SCHNEIDER MARIANE 東京大学, 情報理工学(系)研究科, 外国人特別研究員
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| Project Period (FY) |
2020-11-13 – 2023-03-31
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| Keywords | wastewater treatment / soft sensors / monitoring / control / hybrid modelling / machine learing |
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| Outline of Annual Research Achievements |
1. Ryuichi Watanabe (PhD student at the University of Kyoto) and I could collect sensor data with unmaintained pH and dissolved oxygen sensors. The setup is a lab-scale wastewater treatment plant fed with artificial wastewater. 2. We are currently analysing the data with machine learning techniques. 3. I generated data with a mechanistic activated sludge model to test algorithms used in robotics. 4. I could give an invited talk at the IWA (international water association) WRRmod (Water Resource Recovery Modelling) conference, Arosa, Switzerland 2021 on hybrid modelling. 5. I wrote and submitted a proposal for a two-year postdoc to the Swiss National Science Foundation and got the funding. 6. With the participants from the WRRmod conference we wrote a review article on hybrid modelling and are currently forming a working group. 7. I wrote another article on sensor-influenced monitoring of on-site wastewater treatment plants. This article is not yet submitted. 8. I participated in seminars, lectures, and sub-group meetings in the department of Mechano-informatics, which allowed me to learn a lot about methods used in robotics and start getting a different thinking. 9. During a field visit with Prof. Harada, I could learn about maintenance and control of Japanese on-site wastewater treatment plants (Johkasou), which allowed me to see how advanced the Japanese management of on-site wastewater treatment plants it. 10. I got the opportunity to study Japanese up to the lower intermediate level.
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| Current Status of Research Progress |
Current Status of Research Progress
2: Research has progressed on the whole more than it was originally planned.
Reason
The project is advancing well, despite the inevitably delay of the experiment due to Covid-19. Fortunately, the delay had not fatal influence on the project. The experimental part is slightly behind schedule (Covid-19 spread), but we are catching up and will be able to collect all required data during the Postdoc at the University of Tokyo. Spending less time in the laboratory was even an advantage because it allowed me to build a mechanistic model to additionally generate synthetic data to explore machine learning algorithms.
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| Strategy for Future Research Activity |
1. The experiment in Kyoto reached now stable conditions. So we are collecting data on normal use. In the next few months we will mimic typical failures of on-site wastewater treatment plants in Japan (e.g. Addition of oil) and collect data from the effluent, the tank and observe the wear-and-tear effects of the sensors.
2. Secure my next academic position. I wrote a proposal for a Swiss National Foundation postdoc and got the grant starting from May 2023..
3: Collaborations: Continue the collaborations with the hybrid modelling group, Eawag, and TU Dresden.
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