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Prediction of crude oil product quality parameters using neural networks

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Date Issued:
1996
Summary:
Inferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy neural network model is also proposed that takes into account the fuzziness in both process variables and the corresponding product quality parameter. The training algorithm is derived based on the backpropagation technique. The results of the proposed study can ultimately enhance the on-line prediction of crude oil product quality parameters for crude fractionation processes in the Ras Tanura Oil Refinery.
Title: Prediction of crude oil product quality parameters using neural networks.
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Name(s): Bawazeer, Khalid Ahmed.
Florida Atlantic University, Degree grantor
Zilouchian, Ali, Thesis advisor
Type of Resource: text
Genre: Electronic Thesis Or Dissertation
Issuance: monographic
Date Issued: 1996
Publisher: Florida Atlantic University
Place of Publication: Boca Raton, Fla.
Physical Form: application/pdf
Extent: 116 p.
Language(s): English
Summary: Inferential analysis using neural networks technology is being proposed for the Ras Tanura Refinery crude fractionation section. Plant data for a three month operation period is analyzed in order to construct a neural network model with backpropagation training algorithm. The proposed neural network model can predict various properties associated with crude oil products. The simulation results for modeling Naphtha 95% cut point and Naphtha Reid vapor pressure properties are analyzed. A fuzzy neural network model is also proposed that takes into account the fuzziness in both process variables and the corresponding product quality parameter. The training algorithm is derived based on the backpropagation technique. The results of the proposed study can ultimately enhance the on-line prediction of crude oil product quality parameters for crude fractionation processes in the Ras Tanura Oil Refinery.
Identifier: 15302 (digitool), FADT15302 (IID), fau:12072 (fedora)
Collection: FAU Electronic Theses and Dissertations Collection
Note(s): College of Engineering and Computer Science
Thesis (M.S.)--Florida Atlantic University, 1996.
Subject(s): Petroleum products--Analysis
Petroleum products--Testing
Petroleum industry and trade--Quality control
Neural networks (Computer science)
Held by: Florida Atlantic University Libraries
Persistent Link to This Record: http://purl.flvc.org/fcla/dt/15302
Sublocation: Digital Library
Use and Reproduction: Copyright © is held by the author, with permission granted to Florida Atlantic University to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Use and Reproduction: http://rightsstatements.org/vocab/InC/1.0/
Host Institution: FAU
Is Part of Series: Florida Atlantic University Digital Library Collections.