公开(公告)号：US20220267169A1

公开(公告)日：2022-08-25

申请号：US17676692

申请日：2022-02-21

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Honggui Han ,  Hongxu Liu ,  Junfei Qiao

IPC: C02F1/00 ,  G06N3/04 ,  G06N3/08

Abstract: An intelligent detection system of effluent total nitrogen (TN) based on fuzzy transfer learning algorithm belongs to the field of intelligent detection technology. To detect the TN concentration, the artificial neural network can be used to model wastewater treatment process due to the nonlinear approximation ability and learning ability. However, wastewater treatment process has the characteristic of time-varying dynamics and external disturbance, artificial neural network prediction method cannot acquire sufficient data to ensure the accuracy of TN prediction, and data loss and data deficiency will make the prediction model invalid. The invention proposed an intelligent detection system of effluent total nitrogen based on fuzzy transfer learning algorithm; the proposed system contains several functional modules, including detection instrument, data acquisition, data storage and TN prediction. For the TN prediction module, the fuzzy transfer learning algorithm build the fuzzy neural network based intelligent prediction model, which the parameters are adjusted by the transfer learning method.

公开(公告)号：US10919791B2

公开(公告)日：2021-02-16

申请号：US16143409

申请日：2018-09-26

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Honggui Han ,  Hongxu Liu ,  Jiaming Li ,  Junfei Qiao

IPC: C02F3/00 ,  G06F30/20 ,  C02F3/12 ,  G01N33/18 ,  G06N3/04 ,  G06F111/10

Abstract: An intelligent identification method of sludge bulking based on type-2 fuzzy-neural-network belongs to the field of intelligent detection technology. The sludge volume index (SVI) in wastewater treatment plant is an important index to measure the sludge bulking of activated sludge process. However, poor production conditions and serious random interference in sewage treatment process are characterized by strong coupling, large time-varying and serious hysteresis, which makes the detection of SVI concentration of sludge volume index extremely difficult. At the same time, there are many types of sludge bulking faults, which are difficult to identify effectively. Due to the sludge volume index (SVI) is unable to online monitoring and the fault type of sludge bulking is difficult to determined, the invention develop soft-computing model based on type-2 fuzzy-neural-network to complete the real-time detection of sludge volume index (SVI). Combined with the target-related identification algorithm, the fault type of sludge bulking is determined. Results show that the intelligent identification method can quickly obtain the sludge volume index (SVI), accurate identification fault type of sludge bulking, improve the quality and ensure the safety operation of the wastewater treatment process.