Method for effluent total nitrogen-based on a recurrent self-organizing RBF neural network
US10570024B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 23, 2016 |
| Grant date | Feb 25, 2020 |
| Priority date | — |
| Expiry date | May 1, 2038 |
Classification
- Technology area (CPC C)Chemistry; Metallurgy
- CPC primaryC02F2209/18
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
In this present disclosure, a computing implemented method is designed for predicting the effluent total nitrogen concentration (TN) in an urban wastewater treatment process (WWTP). The technology of this present disclosure is part of advanced manufacturing technology and belongs to both the field of control engineer and environment engineer. To improve the predicting efficiency, a recurrent self-organizing radial basis function (RBF) neural network (RSORBFNN) can adjust the structure and parameters simultaneously. This RSORBFNN is developed to implement this method, and then the proposed RSORBFNN-based method can predict the effluent TN concentration with acceptable accuracy. Moreover, online information of effluent TN concentration may be predicted by this computing implemented method to enhance the quality monitoring level to alleviate the current situation of wastewater and to strengthen the management of WWTP.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.