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    Measuring phosphorus in wastewater using a self-organizing RBF neural network
    1.
    发明授权
    Measuring phosphorus in wastewater using a self-organizing RBF neural network 有权

    公开(公告)号：US10539546B2

    公开(公告)日：2020-01-21

    申请号：US15891175

    申请日：2018-02-07

    申请人： Beijing University of Technology

    发明人： Honggui Han ,  Junfei Qiao ,  Wendong Zhou

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

    摘要： 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.

    Measuring Phosphorus in Wastewater Using a Self-Organizing RBF Neural Network
    3.
    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

    申请人： Beijing University of Technology

    发明人： Honggui Han ,  Junfei Qiao ,  Wendong Zhou

    IPC分类号： G01N33/18 ,  G06N3/08

    CPC分类号： G01N33/18 ,  G06N3/006 ,  G06N3/088

    摘要： 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.

    摘要翻译： 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。