Title of article :
Using Artificial Neural Network for Estimation of Density and Viscosities of Biodiesel–Diesel Blends
Author/Authors :
Moradi, Gholamreza Chemical Engineering Department - Faculty of Engineering - Razi University, Kermanshah , Mohadesi, Majid Chemical Engineering Department - Faculty of Energy - Kermanshah University of Technology, Kermanshah , Karami, Bita Chemical Engineering Department - Faculty of Engineering - Razi University, Kermanshah , Moradi, Ramin Faculty of Mechanical Engineering - Sharif University of Technology, Tehran
Abstract :
In recent years, biodiesel has been considered as a good alternative of diesel fuels. Density and viscosity are two important properties of these fuels. In this study, density and kinematic viscosity of biodiesel-diesel blends were estimated by using artificial neural network (ANN). A three-layer feed forward neural network with Levenberg-Marquard (LM) algorithm was used for learning empirical data (previous studies data and this study empirical data). Input data for estimating density and kinematic viscosity includes components volume fraction, temperature and pure component properties (pure density at 293.15 K and pure kinematic viscosity at 313.15 K). Results of neural network simulation for density and kinematic viscosity showed a high accuracy (mean relative error for density and kinematic viscosity are 0.021% and 0.73%, respectively).
Keywords :
Artificial neural network , Biodiesel , Blend , Density , Kinematic viscosity
Journal title :
Astroparticle Physics
