A data-derived soft-sensor method for monitoring effluent total phosphorus

查看全文 作　　者：Shuguang [1,2,3,4]Zhu;Honggui [1,2]Han;Min [1,3,4]Guo;Junfei [1,2]Qiao

机构地区：[1]College of Electronic Information & Control Engineering, Beijing University of Technology, Beijing 100124, China;[2]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China;[3]Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;[4]Beijing Laboratory for Urban Mass Transit, Beijing 100124, China

出　　处：《Chinese Journal of Chemical Engineering》索引2017年第25卷第12期,共7页

基　　金：Supported by the National Science Foundation of China(61622301,61533002);Beijing Natural Science Foundation(4172005);Major National Science and Technology Project(2017ZX07104) 摘　　要：The effluent total phosphorus(ETP) is an important parameter to evaluate the performance of wastewater treatment process(WWTP). In this study, a novel method, using a data-derived soft-sensor method, is proposed to obtain the reliable values of ETP online. First, a partial least square(PLS) method is introduced to select the related secondary variables of ETP based on the experimental data. Second, a radial basis function neural network(RBFNN) is developed to identify the relationship between the related secondary variables and ETP. This RBFNN easily optimizes the model parameters to improve the generalization ability of the soft-sensor. Finally, a monitoring system, based on the above PLS and RBFNN, named PLS-RBFNN-based soft-sensor system, is developed and tested in a real WWTP. Experimental results show that the proposed monitoring system can obtain the values of ETP online and own better predicting performance than some existing methods. 关键词：导出数据软传感器自河全部的磷废水处理过程光线的基础功能神经网络部分最不方形的方法

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A data-derived soft-sensor method for monitoring effluent total phosphorus

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