DocumentCode :
475739
Title :
Soft Sensor for the Mooney-Viscosity Based on PCA-LSSVM
Author :
Liu, Mei ; Huang, Daoping ; Sun, Zonghai
Author_Institution :
Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou
Volume :
2
fYear :
2008
fDate :
3-4 Aug. 2008
Firstpage :
309
Lastpage :
312
Abstract :
Mooney-viscosity is the dominate quality index for synthetic rubber. Monitoring the Mooney-viscosity effectively and realizing automatic quality control of the production process is an urgent problem in the rubber industry. This paper proposes a soft sensor model based on PCA-LSSVM to predict the Mooney-viscosity of styrene butadiene rubber (SBR). First, major parameters affecting the Mooney-viscosity were chosen based on mechanism analysis. The principal components were extracted by PCA and used as the secondary variables of SVM. Then a soft sensor model for the Mooney-viscosity was established by LSSVM. The simulation results show that the maximum relative error of Mooney-viscosity was low and acceptable at 5.78%. This data may be used to efficiently guide production.
Keywords :
principal component analysis; quality control; rubber industry; support vector machines; viscosity; Mooney-viscosity; automatic quality control; dominate quality index; principal component analysis; production process; rubber industry; soft sensor model; styrene butadiene rubber; support vector machine; synthetic rubber; Automatic control; Automation; Monitoring; Neural networks; Predictive models; Production; Quality control; Rubber industry; Support vector machine classification; Support vector machines; Least Squares Support Vector Machines (LSSVM); Mooney-viscosity; Principal Components Analysis (PCA); Soft sensor;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computing, Communication, Control, and Management, 2008. CCCM '08. ISECS International Colloquium on
Conference_Location :
Guangzhou
Print_ISBN :
978-0-7695-3290-5
Type :
conf
DOI :
10.1109/CCCM.2008.244
Filename :
4609696
Link To Document :
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