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

doi:10.1016/j.cjche.2014.10.004

Chinese Journal of Chemical Engineering ›› 2015, Vol. 23 ›› Issue (1): 138-145.DOI: 10.1016/j.cjche.2014.10.004

• 过程系统工程与过程安全 • 上一篇下一篇

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

College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China

收稿日期:2013-03-18 修回日期:2013-08-28 出版日期:2015-01-28 发布日期:2015-01-24
通讯作者: Qunxiong Zhu
基金资助:
Supported by the National Natural Science Foundation of China (61074153).

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

College of Information Science & Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received:2013-03-18 Revised:2013-08-28 Online:2015-01-28 Published:2015-01-24
Contact: Qunxiong Zhu
Supported by:
Supported by the National Natural Science Foundation of China (61074153).

摘要/Abstract

摘要： 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: Soft sensor, Auto-associative hierarchical neural network, Purified terephthalic acid solvent system, Matter-element

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.

Key words: Soft sensor, Auto-associative hierarchical neural network, Purified terephthalic acid solvent system, Matter-element

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]. Chinese Journal of Chemical Engineering, 2015, 23(1): 138-145.

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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

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