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Journal of Applied Sciences
  Year: 2007 | Volume: 7 | Issue: 17 | Page No.: 2450-2455
DOI: 10.3923/jas.2007.2450.2455
 
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Viscosity Calculation at Moderate Pressure for Nonpolar Gases via Neural Network

A. Bouzidi, S. Hanini , F. Souahi , B. Mohammedi and M. Touiza

Abstract:
A new method, based on Artificial Neural Networks (ANN) of Multi-Layer Perceptron (MLP) type, has been developed to estimate the viscosity at moderate pressure for pure nonpolar gases over a wide range of temperatures. An ANN was trained, using four physicochemical properties: Molecular weight (M), boiling point (Tb), critical Temperature (Tc) and critical Pressure (Pc) combined with absolute Temperature (T) as its inputs, to correlate and predict viscosity. A group of 52 nonpolar gases were used to train and test the performance of the ANN. The viscosity and input data for each individual gas was compiled on average at fifty different temperatures, ranging from the boiling points for each of the chosen gases to 1100 K. The maximum absolute error in viscosity, predicted by the ANN, was approximately 15%.
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How to cite this article:

A. Bouzidi, S. Hanini , F. Souahi , B. Mohammedi and M. Touiza , 2007. Viscosity Calculation at Moderate Pressure for Nonpolar Gases via Neural Network. Journal of Applied Sciences, 7: 2450-2455.

DOI: 10.3923/jas.2007.2450.2455

URL: https://scialert.net/abstract/?doi=jas.2007.2450.2455

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