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Predictions of isolate and normal pentene of debutanizer catalytic reforming unit by using artificial neural network

S., Sulaiman and O.A., Abdalla and M.N., Zakaria and W.F.W., Ahmad (2008) Predictions of isolate and normal pentene of debutanizer catalytic reforming unit by using artificial neural network. In: International Symposium on Information Technology 2008, ITSim, 26 August 2008 through 29 August 2008, Kuala Lumpur.

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Abstract

This paper presents a feed-forward Artificial Neural Network (ANN) model for prediction of isolate and normal pentene of debutanizer catalytic reforming unit. Temperature, reflux flow, and flow rate are used as input variables to the network. Isolate pentene (iC<sub>5</sub>), and normal pentene (nC<sub>5</sub>) are employed as the output variable. About 500 field data collected from PETRONAS Penapisan (Melaka) Sdn Bhd were used to develop the ANN model. The developed ANN model is obtained by dividing the collected data set into three different group; training, validation, and testing group. Back-propagation algorithm was used to train the network. A correlation coefficient of 0.999 was obtained with standard deviation of 0.006 for iC<sub>5</sub>. For nC<sub>5</sub> a 0.999 correlation coefficient and 0.005 standard deviation obtained. © 2008 IEEE.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:Backpropagation; Correlation methods; Forecasting; Image classification; Information technology; Statistical tests; Statistics; Ann models; Artificial neural network models; Artificial neural networks; Back-propagation algorithms; Catalytic reforming; Correlation coefficients; Data sets; Debutanizer; Field datums; Input variables; Output variables; Petronas; Standard deviations; Neural networks
Subjects:Q Science > Q Science (General)
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments / MOR / COE:Departments > Computer Information Sciences
ID Code:99
Deposited By: Dr Suziah Sulaiman
Deposited On:23 Feb 2010 14:01
Last Modified:19 Jan 2017 08:26

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