Prediction of research octane number in catalytic naphtha reforming unit of Shazand Oil Refinery Online publication date: Wed, 13-Jul-2016
by Abdolreza Moghadassi; Alireza Beheshti; Fahime Parvizian
International Journal of Industrial and Systems Engineering (IJISE), Vol. 23, No. 4, 2016
Abstract: In this work, an artificial neural network model was developed to predict the research octane number of an industrial catalytic naphtha reforming unit. Needed date set was provided from Shazand Oil Refinery which was continuously measured. The H2/HC ratio, feed flow rate, pressure, specific gravity and ASTM D-86 distillation data of the feed stream were considered as input variables. Various neural networks were trained and tested in order to find the best ANN model. A three-layer network including two hidden layers was found with minimum mean square error of 0.28 for testing. Comparison between estimated and experimental values of octane number exhibits good agreement. The results show the capability of ANN model to predict the octane number of a typical catalytic naphtha reforming unit with an acceptable error. Therefore, developed ANN model can be applied in other similar units for octane number prediction.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Industrial and Systems Engineering (IJISE):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook