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

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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
- Data-driven intelligent monitoring system for key variables in wastewater treatment process
- 中国化学工程学报（英文版） 2018年26卷第10期页码：2093-2101
- 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
  中图分类号：
- 纸质出版：2018
- Accepted：
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Honggui Han, Shuguang Zhu, Junfei Qiao, 等. Data-driven intelligent monitoring system for key variables in wastewater treatment process[J]. 中国化学工程学报（英文版）, 2018,26(10):2093-2101.

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.
Honggui Han, Shuguang Zhu, Junfei Qiao, 等. Data-driven intelligent monitoring system for key variables in wastewater treatment process[J]. 中国化学工程学报（英文版）, 2018,26(10):2093-2101. DOI： 10.1016/j.cjche.2018.03.027.

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.

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