Volume 18 (2021)
Volume 17 (2020)
Volume 16 (2019)
Volume 15 (2018)
Volume 14 (2017)
Volume 13 (2016)
Volume 12 (2015)
Volume 11 (2014)
Volume 10 (2013)
Volume 9 (2012)
Volume 8 (2011)
Volume 7 (2010)
Volume 6 (2009)
Volume 5 (2008)
Volume 4 (2007)
Volume 3 (2006)
Volume 2 (2005)
Volume 1 (2004)
1. Developing genetic algorithm-based neural networks and sensitivity analysis for thermal conductivity of natural gases

R. Beigzadeh; R. Ozairy

Volume 17, Issue 2 , Spring 2020, , Pages 44-55

http://dx.doi.org/10.22034/ijche.2020.249879.1349

Abstract
  The artificial neural network (ANN) approach was applied to develop simple correlations for predicting the thermal conductivity of nitrogen-methane and carbon dioxide-methane mixtures. The genetic algorithm method was used to obtain global optimum parameters (weights and biases) of the ANNs. The methane ...  Read More

Modeling and Simulation
2. Prediction of true critical temperature and pressure of binary hydrocarbon mixtures: A Comparison between the artificial neural networks and the support vector machine

M. Etebarian; k. movagharnejad

Volume 16, Issue 2 , Spring 2019, , Pages 14-40

Abstract
  Two main objectives have been considered in this paper: providing a good model to predict the critical temperature and pressure of binary hydrocarbon mixtures, and comparing the efficiency of the artificial neural network algorithms and the support vector regression as two commonly used soft computing ...  Read More

Modeling and Simulation
3. Prediction of the pharmaceutical solubility in water and organic solvents via different soft computing models

A. Yousefi; k. movagharnejad

Volume 16, Issue 1 , Winter 2019, , Pages 83-100

Abstract
  Solubility data of solid in aqueous and different organic solvents are very important physicochemical properties considered in the design of the industrial processes and the theoretical studies. In this study, experimental solubility data of 666 pharmaceutical compounds in water and 712 pharmaceutical ...  Read More