Mohd. Yusof, Khairiyah and Karray, Fakhri and Douglas, Peter L. (2003) Development of neural network models for a crude oil distillation column. Jurnal Teknologi F (38F). pp. 53-64. ISSN 0127-9696
Official URL: http://www.penerbit.utm.my/onlinejournal/38/F/JT38...
This paper discusses the development of artificial neural network (ANN) models for a crude oil distillation column. Since the models were developed for real time optimisation (RTO) applications, they are steady-state, multivariable models. Training and testing data used to develop the models were generated from a reconciled steady-state model simulated in a process simulator. The radial basis function networks (RBFN), a type of feedforward ANN model, were able to model the crude tower very well, with the root mean square error for the prediction of each variable less than 1%. Grouping related output variables in a network model was found to give better predictions than lumping all the variables in a single model; this also allowed the overall complex, multivariable model to be simplified into smaller models that are more manageable. In addition, the RBFN models were also able to satisfactorily perform range and dimensional extrapolation, which is necessary for models that are used in RTO.
|Subjects:||T Technology > TP Chemical technology|
|Divisions:||Chemical and Natural Resources Engineering (Formerly known)|
|Deposited By:||Pn Norhayati Abu Ruddin|
|Deposited On:||07 Mar 2007 03:13|
|Last Modified:||06 Jun 2011 07:59|
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