شماره ركورد كنفرانس :
5048
عنوان مقاله :
PREDICTION OF DIMETHYL ETHER DENSITY USING ARTIFICIAL NEURAL NETWORK
Author/Authors :
M.R ،Nikkholgh Department of Chemical Engineering - Faculty of Engineering, Arak University - Arak - Markazi, Iran , A.R ،Moghadassi Department of Chemical Engineering - Faculty of Engineering, Arak University - Arak - Markazi, Iran , F ،Parvizian Department of Chemical Engineering - Faculty of Engineering, Arak University - Arak - Markazi, Iran , A.R ،Agha Aminiha Department of Chemical Engineering - Faculty of Engineering, Arak University - Arak - Markazi, Iran
كليدواژه :
Neural network , Dimethyl ether , Density , Thermodynamic properties
عنوان كنفرانس :
ششمين كنگره بين المللي مهندسي شيمي
چكيده لاتين :
In this work, the ability of Artificial Neural Network based on back-propagation algorithm to
predicting of dimethyl ether density has been investigated. Several feed-forward neural network
with different architectures were tried to determine the best network configuration. The
Levenberg-Marquardt algorithm is applied as the training rule. Comparisons of results show, a
good agreement between experimental data and artificial neural network predictions. Results prove
that artificial neural network can be a successful tool to effectively represent complex nonlinear
systems, if developed efficiently. An important feature of the model is its needlessness to any
theoretical knowledge or human experience during the network training process.
