Prediction of products quality parameters of a crude fractionation section of an oil refinery using neural networks

Author

Bawazeer, K. ; Zilouchian, Ali

Author_Institution

Florida Atlantic Univ., Boca Raton, FL, USA

Volume

1

fYear

1997

fDate

9-12 Jun 1997

Firstpage

157

Abstract

Inferential analysis using neural network technology is proposed for an existing crude fractionation section of an oil refinery. Plant data for a three month operation period is analyzed in order to construct various neural network models using a backpropagation algorithm. The proposed neural networks can predict various properties associated with crude oil productions. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. The results of the proposed work can ultimately enhance the online prediction of crude oil product quality parameters for crude fractionation processes

Keywords

backpropagation; distillation; inference mechanisms; neural net architecture; oil refining; process control; quality control; Naphtha 95% cut point; Naphtha Reid vapor pressure; crude fractionation section; inferential analysis; oil refinery; online prediction; products quality parameters; three month operation period; Artificial neural networks; Data analysis; Fractionation; Neural networks; Neurons; Oil refineries; Performance analysis; Petroleum; Predictive models; Production;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks,1997., International Conference on

Conference_Location

Houston, TX

Print_ISBN

0-7803-4122-8

Type

conf

DOI

10.1109/ICNN.1997.611656

Filename

611656