Multivariate control based on recurrent wavelet neural network for wastewater treatment process
Yu Fang, Yin Su, You Li · 2026
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Description
Yu Fang, Yin Su, and You Li build a self-adjusting controller for wastewater treatment plants, in a 2026 paper. Treatment hinges on many linked chemical reactions, so steering one measure disturbs another. Two key measures, dissolved oxygen and nitrate nitrogen, pull against each other tightly. The authors' controller reads the errors of both at once instead of running separate loops that fight.
The controller is a neural network with a memory loop and wave-shaped units, which can grow or shrink its own structure as conditions change. The authors prove the closed system stays stable using a classic energy-style argument. Tests on a standard treatment simulation showed good control of the process, they report. Smarter control promises cleaner outflow with less energy and fewer chemical doses.
Catalog record
| CREATOR | Yu Fang, Yin Su, You Li |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |