Ahmad, A. and L., W. Piang (2008) Estimation of fatty acid composition using PLS based model. Journal of Chemical & Natural Resources Engineering, 2 . pp. 59-71. ISSN 1823-5255
- Published Version
Official URL: http://www.cheme.utm.my/cheme/index.php/journal-ar...
Given sufficiently rich and appropriate pre-treated data, Partial Least Square (PLS) models are able to accurately represent process dynamics. However, when applied to a fatty acid distillation process over broader ranges of operating conditions, the model was found not adequate. To incorporate nonlinear estimation feature, neural networks were incorporated to form Neural Network (NNPLS) and a further modified method known as Nested NNPLS. The results obtained proved that the Nest-NNPLS model provided the best estimation capability and should therefore be developed further as a potential candidate for on-line estimation of chemical product composition.
|Additional Information:||Productivity Improvement through Optimization|
|Uncontrolled Keywords:||partial least square, inferential estimation, data scaling|
|Subjects:||T Technology > TP Chemical technology|
|Divisions:||Chemical and Natural Resources Engineering (Formerly known)|
|Deposited By:||Ms Zalinda Shuratman|
|Deposited On:||18 Apr 2011 02:10|
|Last Modified:||10 Aug 2012 00:56|
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