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

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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
- Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network
- 中国化学工程学报（英文版） 2015年23卷第1期页码：138-145
- 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
  中图分类号：
- 纸质出版：2015
- Accepted：
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Yanlin He, Yuan Xu, Zhiqiang Geng, 等. Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network[J]. 中国化学工程学报（英文版）, 2015,23(1):138-145.

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.
Yanlin He, Yuan Xu, Zhiqiang Geng, 等. Soft sensor of chemical processes with large numbers of input parameters using auto-associative hierarchical neural network[J]. 中国化学工程学报（英文版）, 2015,23(1):138-145. DOI： 10.1016/j.cjche.2014.10.004.

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.

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