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Radial basis function (RBF) network for modeling gasoline properties
Authors:Afshin Tatar  Ali Barati  Adel Najafi  Amir H Mohammadi
Affiliation:1. Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus, Durban, South AfricaAfshin.Tatar@gmail.com amir_h_mohammadi@yahoo.com;2. Discipline of Chemical Engineering, School of Engineering, University of KwaZulu-Natal, Howard College Campus, Durban, South Africa
Abstract:One of the important products of a crude oil refinery is gasoline which is greatly used as a liquid fuel. Hence, it is necessary to accurately specify its quality by measuring different properties of gasoline. In this study, radial basis function neural networks were utilized for estimation of different characteristics of gasoline including specific gravity (SG), Reid vapor pressure (RVP), research octane number (RON) and motor octane number (MON). The genetic algorithm was used as an optimization algorithm to optimize the maximum neuron number and spread of model. Results reveal that the developed GA-RBF model is effective and precise for estimating experimental data. Furthermore, comparison between the GA-RBF model and a previously reported LSSVM model in literature shows the superiority of GA-RBF model.
Keywords:Gasoline  GA-RBF  model  octane number  reid vapor pressure  specific gravity
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