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Abstract
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In this study, artificial neural network (ANN) and
thermodynamic models were developed for prediction of the
heat capacity (CP) 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 CP 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 CEP
data as function of temperature.
In addition, the effects of temperature and CO2 loading at
different concentrations of conventional amines on the CP
values were investigated. Both models were validated against
experimental data and very good results were obtained between
two mentioned models and experimental data of CP collected
from various literatures. The AARD between ANN model results
and experimental data of CP for 47 systems of aminebased
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.
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