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    Intelligent detection method for Biochemical Oxygen Demand based on a Self-organizing Recurrent RBF Neural Network
    1.
    发明申请
    Intelligent detection method for Biochemical Oxygen Demand based on a Self-organizing Recurrent RBF Neural Network 审中-公开

    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.

    Intelligent detection method for biochemical oxygen demand based on a self-organizing recurrent RBF neural network
    2.
    发明授权
    Intelligent detection method for biochemical oxygen demand based on a self-organizing recurrent RBF neural network 有权

    公开(公告)号：US11346831B2

    公开(公告)日：2022-05-31

    申请号：US15186260

    申请日：2016-06-17

    Applicant: Beijing University of Technology

    Inventor： Honggui Han ,  Yanan Guo ,  Junfei Qiao

    IPC: G01N33/18 ,  G06N3/04 ,  G06N3/08 ,  C02F3/00 ,  C02F3/12 ,  G05B19/406

    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.

    Fault identifying method for sludge bulking based on a recurrent RBF neural network
    3.
    发明授权
    Fault identifying method for sludge bulking based on a recurrent RBF neural network 有权

    公开(公告)号：US11144816B2

    公开(公告)日：2021-10-12

    申请号：US15798263

    申请日：2017-10-30

    Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

    Inventor： Honggui Han ,  Yanan Guo ,  Junfei Qiao

    IPC: G06N3/04 ,  C02F3/00 ,  G06N3/08 ,  C02F3/12

    Abstract: The wastewater treatment process by using activated sludge process often appear the sludge bulking fault phenomenon. Due to production conditions of wastewater treatment process, the correlation and restriction between variables, the characteristics of nonlinear and time-varying, which lead to hard identification of sludge bulking; Sludge bulking is not easy to detect and the reasons resulting in the sludge bulking are difficult to identify, are current RBF neural network is designed for detecting and identifying the causes of sludge volume index (SVI) in this patent. The method builds soft-computing model of SVI based on recurrent RBF neural network, it has been completed to the real-time prediction of SVI concentration and better accuracy were obtained. Once the fault of sludge bulking is detected, the identifying cause variables (CVI) algorithm can find the cause variables of sludge bulking. The method can effectively identify the fault of sludge bulking and ensure the safety operation of the wastewater treatment process.

    Method for effluent total nitrogen-based on a recurrent self-organizing RBF neural network
    4.
    发明授权
    Method for effluent total nitrogen-based on a recurrent self-organizing RBF neural network 有权

    公开(公告)号：US10570024B2

    公开(公告)日：2020-02-25

    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 ,  C02F3/00 ,  G06N3/08 ,  G06N3/04 ,  C02F101/16

    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 radial basis function (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 concentration with acceptable accuracy. Moreover, online information of effluent TN concentration 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.

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