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

سال انتشار

1388

عنوان كنفرانس

ششمين كنگره بين المللي مهندسي شيمي

زبان مدرك

انگليسي

چكيده فارسي

فاقد چكيده

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.

كشور

ايران

تعداد صفحه 2

6

از صفحه

1

تا صفحه

6