Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

2016 ◽  
Vol 24 (4) ◽  
pp. 491-498 ◽  
Author(s):  
Ahmad KhazaiePoul ◽  
M. Soleimani ◽  
S. Salahi
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Disperse Dyes ◽  
Solubility Prediction ◽  
Supercritical Carbon

Research on Polypropylene Dyeing in Supercritical Carbon Dioxide

2011 ◽  
Vol 175-176 ◽  
pp. 646-650 ◽  
Author(s):  
Hong Jie Zhang ◽  
Zhi Li Zhong ◽  
Li Li Feng ◽  
Xiao Ping Quan
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Disperse Dyes ◽  
Polypropylene Fibers ◽  
Temperature And Pressure ◽  
Supercritical Carbon

Polypropylene fibers were dyed with Disperse dyes Blue 2B in Supercritical Carbon Dioxide at different temperature, pressure and time. The K/S value were determined and the effect of as temperature, pressure and dyeing time on the dyeing behaviours of disperse dyes on Polypropylene fibers were discussed. It was found that with the increase of dyeing temperature and pressure, the K/S value increased gradually, and dyeing effect was best after the fiber was dyed at 120 °C, 28 MPa for 20 min.

Connectionist approaches for solubility prediction of n-alkanes in supercritical carbon dioxide

2016 ◽  
Vol 29 (1) ◽  
pp. 295-305 ◽  
Author(s):  
Amin Daryasafar ◽  
Navid Daryasafar ◽  
Mohammad Madani ◽  
Mahdi Kalantari Meybodi ◽  
Mohammad Joukar
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Solubility Prediction ◽  
Supercritical Carbon

Solubility of disperse dyes in supercritical carbon dioxide and ethanol

2006 ◽  
Vol 243 (1-2) ◽  
pp. 107-114 ◽  
Author(s):  
M. Banchero ◽  
A. Ferri ◽  
L. Manna ◽  
S. Sicardi
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Disperse Dyes ◽  
Supercritical Carbon

scholarly journals Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO2

2019 ◽  
Author(s):  
Amin Bemani ◽  
Alireza Baghban ◽  
Shahab Shamshirband
Keyword(s):  
Neural Network ◽  
Carbon Dioxide ◽  
Artificial Neural Network ◽  
Supercritical Carbon Dioxide ◽  
Acid Dissociation ◽  
Support Vector ◽  
Acid Dissociation Constant ◽  
Inference System ◽  
Artificial Neural ◽  
Supercritical Carbon

In the present work, a novel and the robust computational investigation is carried out to estimate solubility of different acids in supercritical carbon dioxide. Four different algorithms such as radial basis function artificial neural network, Multi-layer Perceptron artificial neural network, Least squares support vector machine and adaptive neuro-fuzzy inference system are developed to predict the solubility of different acids in carbon dioxide based on the temperature, pressure, hydrogen number, carbon number, molecular weight, and acid dissociation constant of acid. In the purpose of best evaluation of proposed models, different graphical and statistical analyses and also a novel sensitivity analysis are carried out. The present study proposed the great manners for best acid solubility estimation in supercritical carbon dioxide, which can be helpful for engineers and chemists to predict operational conditions in industries.

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