R. Beigzadeh; R. Ozairy
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 ...
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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 mole fraction, temperature, pressure, and density as effective parameters on thermal conductivity were network input variables. 171 and 180 data points related to the nitrogen-methane and carbon dioxide-methane gas mixtures, respectively, divided to test and train datasets. Simple correlations were obtained due to the small number of optimal neurons in the ANN structures. The mean relative errors of 0.206% and 0.199% for the testing dataset indicate the high accuracy and validation of the correlations. The work indicates that artificial intelligence approaches are very useful for thermal conductivity modeling in natural gases. A sensitivity analysis was performed on all input variables that indicates that the gas mixture density has the greatest impact on the thermal conductivity.
Modeling and Simulation
M. Etebarian; k. movagharnejad
Volume 16, Issue 2 , June 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 ...
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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 methods. In order to have a fair comparison and to achieve the highest efficiency, a comprehensive search method is used in neural network modeling, and a particle swram optimization algorithm for SVM modeling. To compare the accuracy of the models, various criteria such as ARD, MAE, MSE, RAE and R2 are used. The simulation results show that the ARD for the prediction of the true critical temperature and pressure of the binary hydrocarbon mixtures for the final optimized ANN-based model is equal to 0.0161 and 0.0387, respectively. The corressponding ARD value for the SVM-based model is equal to 0.0086 and 0.0091 for critical temperature and pressure, respectively. Simulation results show that although both models have a very high predictive accuracy, the SVM has higher learning speed and accuracy than ANN.
Modeling and Simulation
A. Yousefi; k. movagharnejad
Volume 16, Issue 1 , March 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 ...
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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 compounds in organic solvents were collected from different sources. Three different artificial neural networks including multilayer perceptron, radial basis function and support vector machine were constructed to predict the solubility of these different pharmaceutical compounds in water and different solvents. Molecular weight, melting point, temperature and the number of each functional group in the pharmaceutical compound and organic solvents were selected as the input variables of these three different neural network models. The neural network predictions were compared with the experimental data and the SVR-PSO model with the Average Absolute Relative Deviation equal to 0.0166 for the solubility in water and 0.0707 for solubility in organic compounds was selected as the most accurate model.