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

Shuguang Zhu; Honggui Han; Min Guo; Junfei Qiao

doi:10.1016/j.cjche.2017.06.008

Your Location：

Home >

Browse articles >

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

第25届中国过程控制会议专栏 | Updated：2026-01-06

- A data-derived soft-sensor method for monitoring effluent total phosphorus
- Chinese Journal of Chemical Engineering Vol. 25, Issue 12, Pages: 1791-1797(2017)
- Affiliations：
  
  1. Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing,China,100124
  2. Engineering Research Center of Digital Community, Ministry of Education,Beijing,China,100124
  3. Beijing Laboratory for Urban Mass Transit,Beijing,China,100124
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2017.06.008
  CLC：
- Published：2017
- Accepted：
Scan QR Code
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.
DOI：

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. DOI： 10.1016/j.cjche.2017.06.008.

摘要

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.

关键词

Keywords

references

Views

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Multi-timescale feature extraction method of wastewater treatment process based on adaptive entropy

A modified active disturbance rejection control for a wastewater treatment process

A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method

Data-driven intelligent monitoring system for key variables in wastewater treatment process

Online Predictive Monitoring and Prediction Model for a Periodic Process Through Multiway Non-Gaussian Modeling

Related Author

Hongyan Yang

Xiaolong Wu

Yaqian Zhao

Honggui Han

Min Zuo

Zaiwen Liu

Pengfei Xia

Wei Wei

Related Institution

School of Information Science and Technology, Beijing University of Technology

College of Environmental Sciences and Engineering, Beijing University of Technology

Beijing Technology and Business University, Beijing 100048, China National Engineering Laboratory for Agri-product Quality Traceability, Beijing Technology and Business University, Beijing 100048, China Beijing Key Laboratory of Big Data Technology for Food Safety

2 Beijing Key Laboratory of Computational Intelligence and Intelligent System

3 Engineering Research Center of Digital Community, Ministry of Education

⁰