Intelligent detection method for biochemical oxygen demand based on a self-organizing recurrent RBF neural network
US11346831B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 17, 2016 |
| Grant date | May 31, 2022 |
| Priority date | — |
| Expiry date | Aug 22, 2037 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/082
- WIPO fieldMeasurement
- WIPO sectorInstruments
Abstract
Under conventional techniques, wastewater treatment has many problems such as poor production conditions, serious random interference, strong nonlinear behavior, large time-varying, and serious lagging. These problems cause difficulty in detecting wastewater treatment parameters such as biochemical oxygen demand (BOD) values that are used to monitor water quality. To solve problems associated with monitoring BOD values in real-time, the present disclosure utilizes a self-organizing recurrent RBF neural network designed for intelligent detecting of BOD values. Implementations of the present disclosure build a computing model of BOD values based on the self-organizing recurrent RBF neural network to achieve real-time and more accurate detection of the BOD values (e.g., a BOD concentration). The implementations herein quickly and accurately obtain BOD concentrations and improve the quality and efficiency of wastewater treatment.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.