Prediction of heat capacity of amine solutions using artificial neural network and thermodynamic models for CO2 capture processes

In this study, artificial neural network (ANN) and thermodynamic models were developed for prediction of the heat capacity (C-P) of amine-based solvents. For ANN model, independent variables such as concentration, temperature, molecular weight and CO2 loading of amine were selected as the inputs of the model. The significance of the input variables of the ANN model on the C-P values was investigated statistically by analyzing of correlation matrix. A thermodynamic model based on the Redlich-Kister equation was used to correlate the excess molar heat capacity (C-P(E)) data as function of temperature. In addition, the effects of temperature and CO2 loading at different concentrations of conventional amines on the C-P values were investigated. Both models were validated against experimental data and very good results were obtained between two mentioned models and experimental data of C-P collected from various literatures. The AARD between ANN model results and experimental data of C-P for 47 systems of amine-based solvents studied was 4.3%. For conventional amines, the AARD for ANN model and thermodynamic model in comparison with experimental data were 0.59% and 0.57%, respectively. The results showed that both ANN and Redlich-Kister models can be used as a practical tool for simulation and designing of CO2 removal processes by using amine solutions.