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    Intelligent detection method for biochemical oxygen demand based on a self-organizing recurrent RBF neural network
    11.
    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.

    Measuring Phosphorus in Wastewater Using a Self-Organizing RBF Neural Network
    13.
    发明申请
    Measuring Phosphorus in Wastewater Using a Self-Organizing RBF Neural Network 审中-公开
    使用自组织RBF神经网络测量废水中的磷

    公开(公告)号：US20160123949A1

    公开(公告)日：2016-05-05

    申请号：US14620088

    申请日：2015-02-11

    Applicant: Beijing University of Technology

    Inventor： Honggui Han ,  Junfei Qiao ,  Wendong Zhou

    IPC: G01N33/18 ,  G06N3/08

    CPC classification number: G01N33/18 ,  G06N3/006 ,  G06N3/088

    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 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 reasonably 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.

    Abstract translation: 在各种实施方案中，设计了用于预测城市废水处理过程（WWTP）中的流出物总磷（TP）浓度的方法和系统。 为了提高TP预测的效率，可以建立粒子群优化自组织径向基函数（PSO-SORBF）神经网络。 实现可以调整与神经网络相关联的结构和参数来训练神经网络。 这些实施方案可以相当准确地预测流出物TP浓度，并允许及时测量流出物TP浓度。 实施方案可以进一步收集与估计的流出物TP浓度相关的在线信息。 这可能会提高监测过程的质量，加强污水处理厂的管理。

    Multi-time Scale Model Predictive Control of Wastewater Treatment Process
    14.
    发明申请
    Multi-time Scale Model Predictive Control of Wastewater Treatment Process 有权

    公开(公告)号：US20230004780A1

    公开(公告)日：2023-01-05

    申请号：US17678998

    申请日：2022-02-23

    Applicant: BEIJING UNIVERSITY OF TECHNOLOGY

    Inventor： Honggui Han ,  Shijia Fu ,  Haoyuan Sun ,  Junfei Qiao

    IPC: G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  C02F1/00

    Abstract: A multi-time scale model predictive control method for wastewater treatment process is designed to control the dissolved oxygen concentration and nitrate nitrogen concentration in different time scales to ensure that the effluent quality meets the standard. In view of the difference of time scales in wastewater treatment process caused by different sampling periods of dissolved oxygen concentration and nitrate nitrogen concentration, prediction models with different time scales are firstly designed to unify the prediction outputs to the fast time scale. Then, the gradient descent algorithm is used to solve the optimal solution with fast time scale to control the wastewater treatment system. It not only conforms to the operation characteristics of wastewater treatment process, but also solves the problem of poor operation performance of multiobjective model predictive control caused by different time scales. The experimental results show that the multi-time scale model predictive control method can achieve accurate on-line control of dissolved oxygen concentration and nitrate nitrogen concentration with fast time scales.

    Intelligent Detection System of Effluent Total Nitrogen based on Fuzzy Transfer Learning Algorithm
    15.
    发明申请
    Intelligent Detection System of Effluent Total Nitrogen based on Fuzzy Transfer Learning Algorithm 有权

    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.

    Fault identifying method for sludge bulking based on a recurrent RBF neural network
    16.
    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
    17.
    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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