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Proceedings Paper

Prediction of petrochemical product properties
Author(s): Abhijit S. Pandya; Raisa R. Szabo
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Paper Abstract

A neural network model has been designed to predict certain product properties which can be combined with a multivariate controller to improve the current operation of the crude fraction section of the refinery. The model used to predict the 95% naphtha cutoff point was trained using input vectors made up of 33 field inputs, which in turn were collected from actual refinery data. The model was successful in predicting the 95% cut off with a maximum error of 1.06 degree F in the training phase. In the operational phase the maximum error was 4.63 degree F. The paper also discusses issues related to the development of the specific neural network architecture and learning methodology used for this application.

Paper Details

Date Published: 25 March 1998
PDF: 11 pages
Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998); doi: 10.1117/12.304806
Show Author Affiliations
Abhijit S. Pandya, Florida Atlantic Univ. (United States)
Raisa R. Szabo, Nova Southeastern Univ. (United States)

Published in SPIE Proceedings Vol. 3390:
Applications and Science of Computational Intelligence
Steven K. Rogers; David B. Fogel; James C. Bezdek; Bruno Bosacchi, Editor(s)

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