Estimation of (vapour+liquid) equilibrium of binary systems (tert-butanol+2-ethyl-1-hexanol) and (n-butanol+2-ethyl-1-hexanol) using an artificial neural network

2008 ◽  
Vol 40 (7) ◽  
pp. 1152-1156 ◽  
Author(s):  
H. Ghanadzadeh ◽  
H. Ahmadifar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Binary Systems ◽  
Liquid Equilibrium ◽  
Tert Butanol ◽  
Artificial Neural

Prediction of vapor–liquid equilibrium for binary systems containing HFEs by using artificial neural network

2002 ◽  
Vol 199 (1-2) ◽  
pp. 63-78 ◽  
Author(s):  
Shingo Urata ◽  
Akira Takada ◽  
Junji Murata ◽  
Toshihiko Hiaki ◽  
Akira Sekiya
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Binary Systems ◽  
Liquid Equilibrium ◽  
Vapor Liquid Equilibrium ◽  
Artificial Neural ◽  
Vapor Liquid

scholarly journals Artificial Neural Network Models for Prediction of Density and Kinematic Viscosity of Different Systems of Biofuels and Their Blends with Diesel Fuel. Comparative Analysis

2020 ◽  
Vol 69 (7-8) ◽  
pp. 355-364
Author(s):  
Souad Belmadani ◽  
Mabrouk Hamadache ◽  
Cherif Si-Moussa ◽  
Maamar Laidi ◽  
Salah Hanini
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Kinematic Viscosity ◽  
Binary Systems ◽  
Correlation Coefficients ◽  
Network Models ◽  
Neural Network Models ◽  
Experimental Database ◽  
Artificial Neural

In the present article, two models based on the artificial neural network methodology (ANN) have been optimised to predict the density (ρ) and kinematic viscosity (μ) of different systems of biofuels and their blends with diesel fuel. An experimental database of 1025 points, including 34 systems (15 pure systems, 14 binary systems, and 5 ternary systems) was used for the development of these models. These models use six inputs, which are temperature (T) in the range of −10 – 200 °C, volume fractions (X1, X2, X3) in the range of 0–1, and to distinguish these systems, we used kinematic viscosity at 20 °C in the range of 0.67–74.19 mm2 s–1 and density at 20 °C in the range of 0.7560–0.9188 g cm–3. The best results were obtained with the architecture of {6-26-2: 6 neurons in the input layer – 26 neurons in the hidden layer – 2 neurons in the output layer}. Results of comparison between experimental and simulated values in terms of the correlation coefficients were: R2 = 0.9965 for density, and R2 = 0.9938 for kinematic viscosity. A 238 new database experimental of 4 systems (2 pure systems, 1 binary system, and 1 ternary system) was used to check the accuracy of the two ANN models previously developed. Results of prediction performances in terms of the correlation coefficients were: R2 = 0.9980 for density, and R2 = 0.9653 for kinematic viscosity. Comparison of validation results with those of the other studies shows that the neural network models gave far better results.

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