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

doi:10.1016/j.cjche.2017.06.008

Chinese Journal of Chemical Engineering ›› 2017, Vol. 25 ›› Issue (12): 1791-1797.DOI: 10.1016/j.cjche.2017.06.008

• 第25届中国过程控制会议专栏 • 上一篇下一篇

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

Shuguang Zhu^1,2,3,4, Honggui Han^1,2, Min Guo^1,3,4, Junfei Qiao^1,2

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

收稿日期:2016-11-10 修回日期:2017-04-07 出版日期:2017-12-28 发布日期:2018-01-18
通讯作者: Honggui Han,E-mail address:rechardhan@bjut.edu.cn
作者简介:Shuguang Zhu,E-mail addresses:Zhushuguang@emails.bjut.edu.cn;Min Guo,E-mail addresses:guomin@bjut.edu.cn
基金资助:
Supported by the National Science Foundation of China (61622301, 61533002), Beijing Natural Science Foundation (4172005), and Major National Science and Technology Project (2017ZX07104).

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

Shuguang Zhu^1,2,3,4, Honggui Han^1,2, Min Guo^1,3,4, Junfei Qiao^1,2

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

Received:2016-11-10 Revised:2017-04-07 Online:2017-12-28 Published:2018-01-18
Contact: Honggui Han,E-mail address:rechardhan@bjut.edu.cn
Supported by:
Supported by the National Science Foundation of China (61622301, 61533002), Beijing Natural Science Foundation (4172005), and Major National Science and Technology Project (2017ZX07104).

摘要/Abstract

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: Data-derived soft-sensor, Effluent total phosphorus, Wastewater treatment process, Radial basis function neural network, Partial least square method

Abstract: 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.

Key words: Data-derived soft-sensor, Effluent total phosphorus, Wastewater treatment process, Radial basis function neural network, Partial least square method

Shuguang Zhu, Honggui Han, Min Guo, Junfei Qiao. A data-derived soft-sensor method for monitoring effluent total phosphorus[J]. Chinese Journal of Chemical Engineering, 2017, 25(12): 1791-1797.

Shuguang Zhu, Honggui Han, Min Guo, Junfei Qiao. A data-derived soft-sensor method for monitoring effluent total phosphorus[J]. Chin.J.Chem.Eng., 2017, 25(12): 1791-1797.

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

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

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