公开(公告)号：US11530139B2

公开(公告)日：2022-12-20

申请号：US16694911

申请日：2019-11-25

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Honggui Han ,  Jiaming Li ,  Xiaolong Wu ,  Junfei Qiao

IPC: C02F1/20 ,  G06N20/10 ,  C02F1/58 ,  C02F3/30 ,  G05B13/02 ,  G06F17/16 ,  G06F17/18 ,  G06N3/04 ,  G06N3/08 ,  C02F101/16

Abstract: A cooperative fuzzy-neural control method is designed in this present invention. Due to the difficulty for cooperatively controlling the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process, a cooperative fuzzy-neural control method is investigated. In this proposed method, firstly, a interval type-2 fuzzy neural network is employed to construct the cooperative fuzzy-neural controller. Secondly, a parameter cooperative strategy is proposed to cooperatively optimize the global and local parameters of the cooperative fuzzy-neural controller to meet the control requirements. This proposed cooperative fuzzy-neural control method can cooperatively control the concentrations of the dissolved oxygen and nitrate nitrogen in wastewater treatment process. The results illustrate that the proposed cooperative fuzzy-neural control method can achieve the high control accuracy and guarantee the normal operations of wastewater treatment process under the different operation conditions.

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.