Neural Network Model for Forecasting the Cetane Number in the Diesel Fuels
Petar Halachev
Abstract
The cetane number is important for the process of production of the diesel fuels. This article offers a model for forecasting the cetane number by neural networks. The proposed model is compared with a standardized by the organization ASTM method for forecasting, used in the factories of LUKOIL. For this purpose the laboratories of LUKOIL have tested 140 samples of diesel fuel, and the data, provided by them include the cetane number and the density, plus three more indicators expressed in a scale defined by the organization ASTM, also established empirically. As a result, estimation made by the neural network has less error than the method used in the enterprise.
Neural network, forecasting, diesel fuels.
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[Petar Halachev (2015) Neural Network Model for Forecasting the Cetane Number in the Diesel Fuels IJIRCST Vol-2 Issue-6 Page No-33-39] (ISSN 2347 - 5552). www.ijircst.org
Petar Halachev
Department of Informatics, University of Chemical technology and Metallurgy, Bulgaria, Sofia,
