Development of a robust hybrid estimator using partial least squares regression and artificial neural networks.
Measurement difficulty is one of the process control issues arising from the complexity and the lack of online measurement devices. One of the alternative solutions to deal with the problem is inferential estimation where secondary variables, such as temperature and pressure are used to predict the...
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| Format: | Article |
| Language: | en |
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Universiti Malaysia Sabah
2003
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| Online Access: | http://eprints.utm.my/8024/1/ArshadAhmad2003_DevelopmentOfARobustHybridEstimator.pdf http://eprints.utm.my/8024/ |
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| _version_ | 1845471931942305792 |
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| author | Ahmad, Arshad Lim, Wan Piang |
| author_facet | Ahmad, Arshad Lim, Wan Piang |
| author_sort | Ahmad, Arshad |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Measurement difficulty is one of the process control issues arising from the complexity and the lack of online measurement devices. One of the alternative solutions to deal with the problem is inferential estimation where secondary variables, such as temperature and pressure are used to predict the unmeasured primary variables that are manly product qualities. This paper presents the estimation of product composition for a fatty acid fractionation column using a hybrid technique. The proposed technique combines partial least square regression (PLS) and artificial neural networks (ANN) in an estimation paradigm to provide better estimation properties. The aim is to take advantage of ANN capability to capture the non-linear relationships as well as the statistical strength of PLS method. The results of process estimation using both PLS and hybrid methods are presented. The significant improvement obtained by the hybrid strategy revealed its capability as potentially viable estimator for product properties in chemical industry. |
| format | Article |
| id | my.utm.eprints-8024 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2003 |
| publisher | Universiti Malaysia Sabah |
| record_format | eprints |
| spelling | my.utm.eprints-80242010-06-02T01:50:47Z http://eprints.utm.my/8024/ Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. Ahmad, Arshad Lim, Wan Piang T Technology (General) Measurement difficulty is one of the process control issues arising from the complexity and the lack of online measurement devices. One of the alternative solutions to deal with the problem is inferential estimation where secondary variables, such as temperature and pressure are used to predict the unmeasured primary variables that are manly product qualities. This paper presents the estimation of product composition for a fatty acid fractionation column using a hybrid technique. The proposed technique combines partial least square regression (PLS) and artificial neural networks (ANN) in an estimation paradigm to provide better estimation properties. The aim is to take advantage of ANN capability to capture the non-linear relationships as well as the statistical strength of PLS method. The results of process estimation using both PLS and hybrid methods are presented. The significant improvement obtained by the hybrid strategy revealed its capability as potentially viable estimator for product properties in chemical industry. Universiti Malaysia Sabah 2003 Article PeerReviewed application/pdf en http://eprints.utm.my/8024/1/ArshadAhmad2003_DevelopmentOfARobustHybridEstimator.pdf Ahmad, Arshad and Lim, Wan Piang (2003) Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. Proceedings of International Conference On Chemical and Bioprocess Engineering, 2 . pp. 780-787. |
| spellingShingle | T Technology (General) Ahmad, Arshad Lim, Wan Piang Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. |
| title | Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. |
| title_full | Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. |
| title_fullStr | Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. |
| title_full_unstemmed | Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. |
| title_short | Development of a robust hybrid estimator using partial least squares regression and artificial neural networks. |
| title_sort | development of a robust hybrid estimator using partial least squares regression and artificial neural networks. |
| topic | T Technology (General) |
| url | http://eprints.utm.my/8024/1/ArshadAhmad2003_DevelopmentOfARobustHybridEstimator.pdf http://eprints.utm.my/8024/ |
| url_provider | http://eprints.utm.my/ |