Application of QSPR for prediction of percent conversion of esterification reactions in supercritical carbon dioxide using least squares support vector regression

2010 ◽  
Vol 54 (2) ◽  
pp. 222-230 ◽  
Author(s):  
M. Esteki ◽  
M. Rezayat ◽  
H.S. Ghaziaskar ◽  
T. Khayamian
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Least Squares ◽  
Support Vector Regression ◽  
Support Vector ◽  
Percent Conversion ◽  
Esterification Reactions ◽  
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.

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

2019 ◽  
Author(s):  
Amin Bemani ◽  
Alireza Baghban ◽  
Shahaboddin Shamshirband ◽  
Amir Mosavi ◽  
Peter Csiba ◽  
...  
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 (MLP) artificial neural network (ANN), Least squares support vector machine (LSSVM) and adaptive neuro-fuzzy inference system (ANFIS) 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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