Multiple models soft sensing technique based on online clustering arithmetic for industry distillation

Teng Gang; Bo Cuimei; Lu Bing; Ma Shu

doi:10.1109/WCICA.2014.7053005

Multiple models soft sensing technique based on online clustering arithmetic for industry distillation

Teng Gang, Bo Cuimei, Lu Bing, Ma Shu

COLLEGE OF ELECTRICAL ENGINEERING AND CONTROL SCIENCE

Nanjing Tech University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Aiming at the complex operation and composition online detection problem of the industrial distillation, a multiple model soft sensing based on clustering arithmetic is proposed in the paper in order to realize the online soft sensor. Firstly, the principal component analysis and correlation analysis are used to preprocess a large amount of data set in order to acquire proper modeling sample set. And then, the K-means clustering method was used to analyze the modeling data, the multiple models are established using the partial least squares method. The proposed soft-sensing method was used to predict the composition of the product Butadiene. Practical applications indicated the proposed method was useful for the online prediction of the product quality.

Original language	English
Title of host publication	Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1869-1873
Number of pages	5
Edition	March
ISBN (Electronic)	9781479958252
DOIs	https://doi.org/10.1109/WCICA.2014.7053005
State	Published - 2 Mar 2015
Event	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 - Shenyang, China Duration: 29 Jun 2014 → 4 Jul 2014

Publication series

Name	Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
Number	March
Volume	2015-March

Conference

Conference	2014 11th World Congress on Intelligent Control and Automation, WCICA 2014
Country/Territory	China
City	Shenyang
Period	29/06/14 → 4/07/14

Keywords

Correlation analysis
K-means clustering
Partial least squares
Principal component analysis

Access to Document

10.1109/WCICA.2014.7053005

Cite this

Gang, T., Cuimei, B., Bing, L., & Shu, M. (2015). Multiple models soft sensing technique based on online clustering arithmetic for industry distillation. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014 (March ed., pp. 1869-1873). Article 7053005 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCICA.2014.7053005

Gang, Teng ; Cuimei, Bo ; Bing, Lu et al. / Multiple models soft sensing technique based on online clustering arithmetic for industry distillation. Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 1869-1873 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March).

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title = "Multiple models soft sensing technique based on online clustering arithmetic for industry distillation",

abstract = "Aiming at the complex operation and composition online detection problem of the industrial distillation, a multiple model soft sensing based on clustering arithmetic is proposed in the paper in order to realize the online soft sensor. Firstly, the principal component analysis and correlation analysis are used to preprocess a large amount of data set in order to acquire proper modeling sample set. And then, the K-means clustering method was used to analyze the modeling data, the multiple models are established using the partial least squares method. The proposed soft-sensing method was used to predict the composition of the product Butadiene. Practical applications indicated the proposed method was useful for the online prediction of the product quality.",

keywords = "Correlation analysis, K-means clustering, Partial least squares, Principal component analysis",

author = "Teng Gang and Bo Cuimei and Lu Bing and Ma Shu",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014 ; Conference date: 29-06-2014 Through 04-07-2014",

year = "2015",

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doi = "10.1109/WCICA.2014.7053005",

language = "英语",

series = "Proceedings of the World Congress on Intelligent Control and Automation (WCICA)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Gang, T, Cuimei, B, Bing, L & Shu, M 2015, Multiple models soft sensing technique based on online clustering arithmetic for industry distillation. in Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March edn, 7053005, Proceedings of the World Congress on Intelligent Control and Automation (WCICA), no. March, vol. 2015-March, Institute of Electrical and Electronics Engineers Inc., pp. 1869-1873, 2014 11th World Congress on Intelligent Control and Automation, WCICA 2014, Shenyang, China, 29/06/14. https://doi.org/10.1109/WCICA.2014.7053005

Multiple models soft sensing technique based on online clustering arithmetic for industry distillation. / Gang, Teng; Cuimei, Bo; Bing, Lu et al.
Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March. ed. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1869-1873 7053005 (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); Vol. 2015-March, No. March).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Multiple models soft sensing technique based on online clustering arithmetic for industry distillation

AU - Gang, Teng

AU - Cuimei, Bo

AU - Bing, Lu

AU - Shu, Ma

PY - 2015/3/2

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N2 - Aiming at the complex operation and composition online detection problem of the industrial distillation, a multiple model soft sensing based on clustering arithmetic is proposed in the paper in order to realize the online soft sensor. Firstly, the principal component analysis and correlation analysis are used to preprocess a large amount of data set in order to acquire proper modeling sample set. And then, the K-means clustering method was used to analyze the modeling data, the multiple models are established using the partial least squares method. The proposed soft-sensing method was used to predict the composition of the product Butadiene. Practical applications indicated the proposed method was useful for the online prediction of the product quality.

AB - Aiming at the complex operation and composition online detection problem of the industrial distillation, a multiple model soft sensing based on clustering arithmetic is proposed in the paper in order to realize the online soft sensor. Firstly, the principal component analysis and correlation analysis are used to preprocess a large amount of data set in order to acquire proper modeling sample set. And then, the K-means clustering method was used to analyze the modeling data, the multiple models are established using the partial least squares method. The proposed soft-sensing method was used to predict the composition of the product Butadiene. Practical applications indicated the proposed method was useful for the online prediction of the product quality.

KW - Correlation analysis

KW - K-means clustering

KW - Partial least squares

KW - Principal component analysis

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Gang T, Cuimei B, Bing L, Shu M. Multiple models soft sensing technique based on online clustering arithmetic for industry distillation. In Proceeding of the 11th World Congress on Intelligent Control and Automation, WCICA 2014. March ed. Institute of Electrical and Electronics Engineers Inc. 2015. p. 1869-1873. 7053005. (Proceedings of the World Congress on Intelligent Control and Automation (WCICA); March). doi: 10.1109/WCICA.2014.7053005

Multiple models soft sensing technique based on online clustering arithmetic for industry distillation

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