Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network

Yanlin He; Yuan Xu; Zhiqiang Geng; Qunxiong Zhu

doi:10.1016/j.cjche.2014.10.004

Your Location：

Home >

Browse articles >

Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network

过程系统工程与过程安全 | Updated：2026-01-06

- Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network
- Chinese Journal of Chemical Engineering Vol. 23, Issue 1, Pages: 138-145(2015)
- Affiliations：
  
  College of Information Science & Technology, Beijing University of Chemical Technology,Beijing,China,100029
- Author bio：
- Funds：
- DOI：10.1016/j.cjche.2014.10.004
  CLC：
- Published：2015
- Accepted：
Scan QR Code
Yanlin He, Yuan Xu, Zhiqiang Geng, Qunxiong Zhu . Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network[J]. Chin.J.Chem.Eng., 2015, 23(1): 138-145.
DOI：

Yanlin He, Yuan Xu, Zhiqiang Geng, Qunxiong Zhu . Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network[J]. Chin.J.Chem.Eng., 2015, 23(1): 138-145. DOI： 10.1016/j.cjche.2014.10.004.

摘要

Abstract

To explore the problems of monitoring chemical processes with large numbers of input parameters

a method based on Auto-associative Hierarchical Neural Network (AHNN) is proposed. AHNN focuses on dealing with datasets in high-dimension. AHNNs consist of two parts: groups of subnets based on well trained Autoassociative Neural Networks (AANNs) and a main net. The subnets play an important role on the performance of AHNN. A simple but effective method of designing the subnets is developed in this paper. In this method

the subnets are designed according to the classification of the data attributes. For getting the classification

an effective method called Extension Data Attributes Classification (EDAC) is adopted. Soft sensor using AHNN based on EDAC (EDAC-AHNN) is introduced. As a case study

the production data of Purified Terephthalic Acid (PTA) solvent system are selected to examine the proposed model. The results of the EDAC-AHNN model are compared with the experimental data extracted from the literature

which shows the efficiency of the proposed model.

关键词

Keywords

references

Views

195

Downloads

CSCD

Alert me when the article has been cited

Submit

Tools

Publicity Resources

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

Active training sample selection and updating strategy for near-infrared model with an industrial application

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

Modeling and identification for soft sensor systems based on the separation of multi-dynamic and static characteristics

Feasibility analysis and online adjustment of constraints in model predictive control integrated with soft sensor

Related Author

Du Jian

Zhang Lei

Wang Yu

Li Fan

Li Yi

Tao Jin

Zhang Zhongyi

Zhuang Yu

Related Institution

COFCO Biotechnology Co., Ltd.

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

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology

College of Electrical Engineering and Automation, Shandong University of Science and Technology

4 Beijing Laboratory for Urban Mass Transit

⁰