Fault identifying method for sludge bulking based on a recurrent RBF neural network
US11144816B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 30, 2017 |
| Grant date | Oct 12, 2021 |
| Priority date | — |
| Expiry date | Jul 13, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02W10/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The wastewater treatment process by using activated sludge process often appear the sludge bulking fault phenomenon. Due to production conditions of wastewater treatment process, the correlation and restriction between variables, the characteristics of nonlinear and time-varying, which lead to hard identification of sludge bulking; Sludge bulking is not easy to detect and the reasons resulting in the sludge bulking are difficult to identify, are current RBF neural network is designed for detecting and identifying the causes of sludge volume index (SVI) in this patent. The method builds soft-computing model of SVI based on recurrent RBF neural network, it has been completed to the real-time prediction of SVI concentration and better accuracy were obtained. Once the fault of sludge bulking is detected, the identifying cause variables (CVI) algorithm can find the cause variables of sludge bulking. The method can effectively identify the fault of sludge bulking and ensure the safety operation of the wastewater treatment process.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.