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25 March 1998 Prediction of petrochemical product properties
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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.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Abhijit S. Pandya and Raisa R. Szabo "Prediction of petrochemical product properties", Proc. SPIE 3390, Applications and Science of Computational Intelligence, (25 March 1998);

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