公开(公告)号：US20170185892A1

公开(公告)日：2017-06-29

申请号：US15186260

申请日：2016-06-17

Applicant: Beijing University of Technology

Inventor： Honggui Han ,  Yanan Guo ,  Junfei Qiao

IPC: G06N3/08 ,  G05B19/406

CPC classification number: G05B19/406 ,  G06N3/0445 ,  G06N3/082 ,  Y02P80/114

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 problem cause difficult detection of various wastewater treatment parameter 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.

公开(公告)号：US09633307B2

公开(公告)日：2017-04-25

申请号：US14668836

申请日：2015-03-25

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Junfei Qiao ,  Ying Hou ,  Honggui Han ,  Wenjing Li

IPC: G06N3/08 ,  G01N33/18 ,  G06N3/04 ,  C02F3/00 ,  C02F101/16

CPC classification number: G06N3/088 ,  C02F3/006 ,  C02F2101/16 ,  C02F2209/001 ,  C02F2209/006 ,  C02F2209/04 ,  C02F2209/10 ,  C02F2209/14 ,  C02F2209/18 ,  C02F2209/22 ,  C02F2209/40 ,  G01N33/18 ,  G01N33/188 ,  G06N3/0445

Abstract: An intelligent method is designed for predicting the effluent ammonia-nitrogen concentration in the urban wastewater treatment process (WWTP). The technology of this invention is part of advanced manufacturing technology, belongs to both the field of control engineering and environment engineering. In order to improve the predicting efficiency, a recurrent self-organizing neural network, which can adjust the structure and parameters concurrently to train the parameters, is developed to design this intelligent method. This intelligent method can predict the effluent ammonia-nitrogen concentration with acceptable accuracy and solve the problem that the effluent ammonia-nitrogen concentration is difficult to be measured online. Moreover, the online information of effluent ammonia-nitrogen concentration, predicted by this intelligent method, can enhance the quality monitoring level and alleviate the current situation of wastewater to strengthen the whole management of WWTP.

公开(公告)号：US20220194830A1

公开(公告)日：2022-06-23

申请号：US17691096

申请日：2022-03-09

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Honggui Han ,  Lu Zhang ,  Junfei Qiao

IPC: C02F3/00 ,  G05B13/04 ,  C02F3/02

Abstract: In a cooperative optimal control system, firstly, two-level models are established to capture the dynamic features of different time-scale performance indices. Secondly, a data-driven assisted model based cooperative optimization algorithm is developed to optimize the two-level models, so that the optimal set-points of dissolved oxygen and nitrate nitrogen can be acquired. Thirdly, a predictive control strategy is designed to trace the obtained optimal set-points of dissolved oxygen and nitrate nitrogen. This proposed cooperative optimal control system can effectively deal with the difficulties of formulating the dynamic features and acquiring the optimal set-points.

公开(公告)号：US20180029900A1

公开(公告)日：2018-02-01

申请号：US15389755

申请日：2016-12-23

Applicant: Beijing University of Technology

Inventor： Honggui Han ,  Yanan Guo ,  Junfei Qiao

IPC: C02F1/00 ,  G06N3/08 ,  G06N3/04

CPC classification number: C02F1/008 ,  C02F3/006 ,  C02F2101/16 ,  C02F2209/003 ,  C02F2209/08 ,  C02F2209/14 ,  C02F2209/15 ,  C02F2209/16 ,  C02F2209/18 ,  G06N3/0445 ,  G06N3/0481 ,  G06N3/08 ,  G06N3/082 ,  G06N3/088

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 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 with acceptable accuracy. Moreover, online information of effluent TN 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.

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.

公开(公告)号：US10788473B2

公开(公告)日：2020-09-29

申请号：US15206702

申请日：2016-07-11

Applicant: Beijing University of Technology

Inventor： Junfei Qiao ,  Ying Hou ,  Honggui Han

IPC: G01N33/18

Abstract: A computing system is designed for measuring the A2/O effluent total phosphorus based on data-driven method. Several related variables are obtained by analyzing the relationship between effluent total phosphorus and other process variables. In addition, a hardware platform is designed and built to further analysis sample information of each variable. Finally, the computing system for measuring total phosphorus in effluent is developed by combining the hardware and software as provided in implementations herein.

公开(公告)号：US10539546B2

公开(公告)日：2020-01-21

申请号：US15891175

申请日：2018-02-07

Applicant: Beijing University of Technology

Inventor： Honggui Han ,  Junfei Qiao ,  Wendong Zhou

IPC: G01N31/00 ,  G01N33/18 ,  G06N3/08 ,  C02F3/30 ,  C02F3/00 ,  G06N3/00 ,  G06N3/04 ,  C02F101/10

Abstract: In various implementations, methods and systems are designed for predicting effluent total phosphorus (TP) concentrations in an urban wastewater treatment process (WWTP). To improve the efficiency of TP prediction, a particle swarm optimization self-organizing radial basis function (PSO-SORBF) neural network may be established. Implementations may adjust structures and parameters associated with the neural network to train the neural network. The implementations may predict the effluent TP concentrations with reasonable accuracy and allow timely measurement of the effluent TP concentrations. The implementations may further collect online information related to the estimated effluent TP concentrations. This may improve the quality of monitoring processes and enhance management of WWTP.

公开(公告)号：US12105075B2

公开(公告)日：2024-10-01

申请号：US17472433

申请日：2021-09-10

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Honggui Han ,  Chenxuan Sun ,  Junfei Qiao

IPC: G06N3/043 ,  C02F1/00 ,  G01N33/18 ,  G06N3/08

CPC classification number: G01N33/1806 ,  G06N3/043 ,  G06N3/08 ,  C02F1/008 ,  C02F2209/16

Abstract: A total nitrogen intelligent detection system based on multi-objective optimized fuzzy neural network belongs to both the field of environment engineer and control engineer. The total nitrogen in wastewater treatment process is an important index to measure the quality of effluent. However, it is extremely difficult to detect the total nitrogen concentration due to the long detection time and the low prediction accuracy in the wastewater treatment process. To solve the problem, multi-objective optimized fuzzy neural network with global optimization capability may be established to optimize the structure and parameters to solve the problem of the poor generalization ability of fuzzy neural network. The experimental results show that total nitrogen intelligent detection system can automatically collect the variables information of wastewater treatment process and predict total nitrogen concentration. Meanwhile, in this system, the detection method can improve the prediction accuracy, as well as ensure the total nitrogen concentration be obtained in real-time and low-cost.

公开(公告)号：US20220383062A1

公开(公告)日：2022-12-01

申请号：US17678949

申请日：2022-02-23

Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

Inventor： Honggui Han ,  Xing Bai ,  Ying Hou ,  Hongyan Yang ,  Junfei Qiao

IPC: G06N3/00 ,  G05B19/05

Abstract: An optimal control method for wastewater treatment process (WWTP) based on a self-adjusting multi-task particle swarm optimization (SA-MTPSO) algorithm belongs to the field of WWTP. To balance the relationship between the effluent water quality (EQ) and energy consumption (EC) and achieve optimization online quickly, the invention establishes a data-based multi-task optimization model for WWTP to describe the relationship between the control variables and EQ, EC. Then, the SA-MTPSO algorithm is adopted to solve the optimal set-points of the nitrate nitrogen and dissolved oxygen concentration for WWTP. The PID controller is used to track the optimal set-points, so as to reduce EC while ensuring EQ, and realize the online optimal control of WWTP.