公开(公告)号：US20200024168A1

公开(公告)日：2020-01-23

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 ,  C02F3/12 ,  G01N33/18 ,  G06N3/04 ,  G06F17/50

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.