Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in HDS process

An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS) process. For this purpose, a reliable data- driven soft sensors utilizing Support Vector Regression (SVR) was developed and the effects of integratin...

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Bibliographic Details
Main Authors: Shokri Saeid, Sadeghi Mohammad Taghi, Marvast Mahdi Ahmadi, Narasimhan Shankar
Format: Article
Language:English
Published: Association of the Chemical Engineers of Serbia 2015-01-01
Series:Chemical Industry and Chemical Engineering Quarterly
Subjects:
Principal Component Analysis (PCA)
Vector Quantization (VQ)
Support Vector Regression (SVR)
Soft Sensor
Hydrodesulfurization (HDS) Process
Online Access:http://www.doiserbia.nb.rs/img/doi/1451-9372/2015/1451-93721400039S.pdf

Internet

http://www.doiserbia.nb.rs/img/doi/1451-9372/2015/1451-93721400039S.pdf

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