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

Honggui Han; Shuguang Zhu; Junfei Qiao; Min Guo

doi:10.1016/j.cjche.2018.03.027

Your Location：

Home >

Browse articles >

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

Process Systems Engineering and Process Safety | Updated：2026-01-06

- Data-driven intelligent monitoring system for key variables in wastewater treatment process
- Chinese Journal of Chemical Engineering Vol. 26, Issue 10, Pages: 2093-2101(2018)
- Affiliations：
  
  1. 2 Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing,China,100124
  2. 3 Engineering Research Center of Digital Community, Ministry of Education,Beijing,China,100124
  3. 4 Beijing Laboratory for Urban Mass Transit,Beijing,China,100124
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2018.03.027
  CLC：
- Published：2018
- Accepted：
Scan QR Code
Honggui Han, Shuguang Zhu, Junfei Qiao, Min Guo. Data-driven intelligent monitoring system for key variables in wastewater treatment process[J]. Chin.J.Chem.Eng., 2018, 26(10): 2093-2101.
DOI：

Honggui Han, Shuguang Zhu, Junfei Qiao, Min Guo. Data-driven intelligent monitoring system for key variables in wastewater treatment process[J]. Chin.J.Chem.Eng., 2018, 26(10): 2093-2101. DOI： 10.1016/j.cjche.2018.03.027.

摘要

Abstract

In wastewater treatment process (WWTP)

the accurate and real-time monitoring values of key variables are crucial for the operational strategies. However

most of the existing methods have difficulty in obtaining the real-time values of some key variables in the process. In order to handle this issue

a data-driven intelligent monitoring system

using the soft sensor technique and data distribution service

is developed to monitor the concentrations of effluent total phosphorous (TP) and ammonia nitrogen (NH

-N). In this intelligent monitoring system

a fuzzy neural network (FNN) is applied for designing the soft sensor model

and a principal component analysis (PCA) method is used to select the input variables of the soft sensor model. Moreover

data transfer software is exploited to insert the soft sensor technique to the supervisory control and data acquisition (SCADA) system. Finally

this proposed intelligent monitoring system is tested in several real plants to demonstrate the reliability and effectiveness of the monitoring performance.

关键词

Keywords

references

Views

382

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

Industrial quality prediction via FVAE-WGAN: A data-driven approach

Attention-enhanced multi-time scale LSTM for soft sensor modeling of corn starch liquefaction

Two-layer model for the early warning and analysis of condensate water quality abnormalities based on autoencoder and expert knowledge

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

A modified active disturbance rejection control for a wastewater treatment process

Related Author

Yun Pengxue

Chen Mengyi

Yan Jing

Gao Shiwei

Li Yi

Zhuang Yu

Zhang Zhongyi

Tao Jin

Related Institution

School of Computer Engineering and Science, Northwest Normal University

COFCO Biotechnology Co., Ltd.

Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology

Xi'an Thermal Power Research Institute Co., Ltd.

China Huaneng Group Co., Ltd. Guangxi Branch

⁰