Composition Prediction of a Debutanizer Column using Equation Based Artificial Neural Network Model

Ramli, N.M. and Hussain, M.A. and Mohamed Jan, B. and Abdullah, B. (2014) Composition Prediction of a Debutanizer Column using Equation Based Artificial Neural Network Model. Neurocomputing, 131. pp. 59-76. ISSN 0925-2312

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Official URL: http://www.sciencedirect.com/science/article/pii/S...

Abstract

Debutanizer column is an important unit operation in petroleum refining industries. The design of online composition prediction by using neural network will help improve product quality monitoring in an oil refinery industry by predicting the top and bottom composition of n-butane simultaneously and accurately for the column. The single dynamic neural network model can be used and designed to overcome the delay introduced by lab sampling and can be also suitable for monitoring purposes. The objective of this work is to investigate and implement an artificial neural network (ANN) for composition prediction of the top and bottom product of a distillation column simultaneously. The major contribution of the current work is to develop these composition predictions of n-butane by using equation based neural network (NN) models. The composition predictions using this method is compared with partial least square (PLS) and regression analysis (RA) methods to show its superiority over these other conventional methods. Based on statistical analysis, the results indicate that neural network equation, which is more robust in nature, predicts better than the PLS equation and RA equation based methods.

Item Type: Article
Uncontrolled Keywords: Statistical analysis; Neural network; Partial least square analysis; Regression analysis; Debutanizer column
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TP Chemical technology
Divisions: Faculty of Engineering > School of Chemistry
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 12 Jan 2015 07:09
Last Modified: 12 Jan 2015 07:09
URI: http://eprints.um.edu.my/id/eprint/11903

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