Artificial Neural Network-group Contribution Research Articles

Unified artificial neural network-group contribution method for predictions of normal boiling point and critical temperature of refrigerants and related compounds

In this study, both normal boiling point and critical temperature of refrigerants and related compounds are predicted only from their molecular structures using a simple and unified Artificial Neural Network - Group Contribution Method. Identical 32 (including molecular mass) groups and methodologies have been used with 251 experimental data for TB and 132 experimental data for TC. In spite of its simplicity, the agreements between experimental and ANN predicted data for TB and TC are very good, better than most of the existing models. The percentage errors for training and test data sets are 2.4% and 3.7% and 2.8% and 5.7% for TB and TC respectively. The overall percentage errors for TB and TC are 2.8% and 3.7% respectively. A comparison of the proposed models with other models shows that for the class of compounds considered i.e., refrigerants and related compounds, this model predicts most accurately. These models can be conveniently used for any preliminary screening of compounds as alternative refrigerants or working fluids or for any other applications.

Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method

Abstract In this study, the viscosity of hydrocarbon binary mixtures has been predicted with an artificial neural network and a group contribution method (ANN-GCM) by utilizing various training algorithm including Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Resilient back Propagation (RP), and Gradient Descent with variable learning rate back propagation (GDX). Moreover, different transfer functions such as Tan-sigmoid (tansig), Log-sigmoid (logsig), and purelin were investigated in hidden and output layer and their effects on network precision were estimated. Accordingly, 796 experimental data points of viscosity of hydrocarbon binary mixture were collected from the literature for a wide range of operating parameters. The temperature, pressure, mole fraction, molecular weight, and structural group of the system were selected as the independent input parameters. The statistical analysis results with R 2 = 0.99 revealed a small value for Average absolute relative deviation (AARD) of 1.288 and Mean square error (MSE) of 0.001018 by comparing the ANN predicted data with experimental data. Neural network configuration was also optimized. Based on the results, the network with one hidden layer and 27 neurons with the Levenberg-Marquardt training algorithm and tansig transfer function for hidden layer along with purelin transfer function for output layer constituted the best network structure. Further, the weights and bias were optimized to minimize the error. Then, the obtained results of the present study were compared with the data from some previous methods. The results suggested that this work can predict the viscosity of hydrocarbon binary mixture with better AARD. In general, the results indicated that combining ANN and GCM model is capable to predict the viscosity of hydrocarbon binary mixtures with a good accuracy.

Prediction of the Liquid Vapor Pressure Using the Artificial Neural Network-Group Contribution Method

In this paper, vapor pressure for pure compounds is estimated using the Artificial Neural Networks and a simple Group Contribution Method (ANN-GCM). For model comprehensiveness, materials were chosen from various families. Most of materials are from 12 families. Vapor pressure data of 100 compounds is used to train, validate and test the ANN-GCM model. Vapor pressure data were taken from literature for wide ranges of temperature (68.55-559.15 K). Based on results, the best structure for feed-forward back propagation neural network is Levenberg-Marquardt back propagation training algorithm, logsig transfer function for hidden layer and linear transfer function for output layer. The multiplayer network model consists of temperature, acentric factor, critical temperature, critical pressure and the structure of molecules as inputs, 10 neurons in the hidden layer and one neuron in the output layer corresponding to vapor pressure. The weights are optimized to minimize error between experimental and calculated data. Results show that optimum neural network architecture is able to predict vapor pressure data with an acceptable level. The trained network predicts the vapor pressure data with average relative deviation percent of 1.18%.

Artificial Neural Network-Group Contribution Method for Predicting Standard Enthalpy of Formation in the Solid State: C–H, C–H–O, C–H–N, and C–H–N–O Compounds

In this work, an artificial neural network-group contribution model is developed to predict the standard enthalpy of formation in the solid (crystal) state of pure compounds. Several classes of hydrocarbon compounds CH, oxygenated compounds CHO, nitrogen compounds CHN, and energetic compounds CHNO are investigated to propose a comprehensive and predictive model. The new model is developed and tested for 1222 organic compounds containing complex molecular structures. The performance of the new model has been compared with previous work and is shown to be far more accurate. The obtained results show an average absolute deviation of $$9.33 \,\hbox {kJ}{\cdot }\hbox {mol}^{-1}$$ and a coefficient of determination of 0.9972 for the experimental values.

Liquid density prediction of five different classes of refrigerant systems (HCFCs, HFCs, HFEs, PFAs and PFAAs) using the artificial neural network-group contribution method

In this work, the densities of 48 refrigerant systems from 5 different categories including hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), hydrofluoroethers (HFEs), perfluoroalkanes (PFAs), and perfluoroalkylalkanes (PFAAs) have been studied using a combined method that includes an artificial neural network (ANN) and a simple group contribution method (GCM). A total of 3825 data points of liquid density at several temperatures and pressures have been used to train, validate and test the model. This study shows that the ANN-GCM model represents an excellent alternative to estimate the density of different refrigerant systems with a good accuracy. The average absolute deviations for train, validation, and test sets are 0.18, 0.26, and 0.28, respectively. A comparison between our results and those obtained from some previous methods shows that as well as generality, this model can predict the density of different refrigerants in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions.

Prediction of the specific volume of polymeric systems using the artificial neural network-group contribution method

In this work, the specific volumes of some polymeric systems have been estimated using a combined method that includes an artificial neural network (ANN) and a simple group contribution method (GCM). A total of 2865 data points of specific volume at several temperatures and pressures, corresponding to 25 different polymeric systems have been used to train, validate and test the model. This study shows that the ANN–GCM model represent an excellent alternative for the estimation of the specific volume of different polymeric systems with a good accuracy. The average relative deviations for train, validation, and test sets are 0.0403, 0.0439, and 0.0482, respectively. A wide comparison between our results and those of obtained from some previous methods show that this work can provide a simple procedure for prediction the specific volume of different polymeric systems in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions

Prediction of hydrocarbon densities using an artificial neural network–group contribution method up to high temperatures and pressures

In this work, the densities of hydrocarbon systems have been estimated using a combined method that includes an artificial neural network (ANN) and a simple group contribution method (GCM). A total of 2891 data points of density at several temperatures and pressures, corresponding to 40 different hydrocarbons including short- and long-chain alkanes ranging from CH4 to n-C40H82, and also several cycloalkanes, highly branched alkanes and aromatic hydrocarbons have been used to train, validate and test the model. This study shows that the ANN–GCM model represent an excellent alternative for the estimation of the density of hydrocarbons with a good accuracy. A wide comparison between our results and those of obtained from some previous methods shows that this work can provide a simple procedure for prediction the density of different classes of hydrocarbons in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions.

Prediction of Standard Enthalpy of Combustion of Pure Compounds Using a Very Accurate Group-Contribution-Based Method

The artificial neural network–group contribution (ANN–GC) method is applied to estimate the standard enthalpy of combustion of pure chemical compounds. A total of 4590 pure compounds from various c...

Determination of Critical Properties and Acentric Factors of Pure Compounds Using the Artificial Neural Network Group Contribution Algorithm

In this article, artificial neural network group contribution (ANN-GC) method is applied to calculate and estimate critical properties including the critical pressure, temperature, and volume and acentric factors of pure compounds. About 1700 chemical compounds from various chemical families have been investigated to propose a comprehensive and predictive model. Using this dedicated model, we obtain satisfactory results quantified by the following absolute average deviations of the calculated and estimated properties from existing experimental values: 1.1 % for critical pressure, 0.9 % for critical temperature, 1.4 % for critical volume, and 3.7 % for acentric factor.

Representation and Prediction of Molecular Diffusivity of Nonelectrolyte Organic Compounds in Water at Infinite Dilution Using the Artificial Neural Network-Group Contribution Method

The determination of diffusion coefficients of pure compounds in water at infinite dilution is of utmost interest in chemical and environmental engineering, especially wastewater treatment processes. In this work, the artificial neural network-group contribution (ANN-GC) method is applied to represent and predict the molecular diffusivity of nonelectrolyte organic compounds in water at infinite dilution and 298.15 K. A total of 4852 pure compounds from various chemical families has been investigated to propose a predictive model. The obtained results show the squared correlation coefficient of 0.996, root-mean-square error of about 0.02, and average absolute deviation lower than 1.5 % for the calculated or predicted property from existing experimental values.

Use of Artificial Neural Network-Group Contribution Method to Determine Surface Tension of Pure Compounds

This work aims at applying an artificial neural network-group contribution method to represent/predict the surface tension of pure chemical compounds at different temperatures and atmospheric pressure. To propose a comprehensive, reliable, and predictive tool, about 4700 data belonging to experimental surface tension values of 752 chemical compounds at different temperatures and atmospheric pressure have been studied. The investigated compounds belong to 78 chemical families containing 151 functional groups (group contributions), which include organic and inorganic liquids. Using this dedicated strategy, we obtain satisfactory results quantified by the following statistical parameters: absolute average deviations of the represented/predicted properties from existing experimental values, 1.7 %, and squared correlation coefficient, 0.997.

Prediction of Vaporization Enthalpy of Pure Compounds using a Group Contribution-Based Method

In this work, the artificial neural network–group contribution (ANN-GC) method is applied to estimate the vaporization enthalpy of pure chemical compounds at their normal boiling point. A group of 4907 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient (R2) of 0.993, root mean square error of 1.1 kJ/mol, and average absolute deviation lower than 1.5% for the estimated properties from existing experimental values.

Prediction of enthalpy of fusion of pure compounds using an Artificial Neural Network-Group Contribution method

In this work, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to estimate the enthalpy of fusion of pure chemical compounds at their normal melting point. 4157 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the Squared Correlation Coefficient ( R 2) of 0.999, Root Mean Square Error of 0.82 kJ/mol, and average absolute deviation lower than 2.65% for the estimated properties from existing experimental values.

An accurate model for prediction of autoignition temperature of pure compounds

Accurate prediction of pure compounds autoignition temperature (AIT) is of great importance. In this study, the Artificial Neural Network-Group Contribution (ANN-GC) method is applied to evaluate the AIT of pure compounds. 1025 pure compounds from various chemical families are investigated to propose a comprehensive and predictive model. The obtained results show the squared correlation coefficient of 0.984, root mean square error of 15.44 K, and average percent error of 1.6% for the experimental values.