Abstract
In this study, Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were developed to estimate the equilibrium solubility and partial pressure of CO2 in blended aqueous solutions of diisopropanolamine (DIPA) and 2-amino-2-methylpropanol (AMP). In this study, several key parameters were analyzed to understand the behavior of the aqueous DIPA/AMP system for CO2 capture. Including DIPA (9–21 wt%), AMP (9–21 wt%), temperature (323.15–358.15 K), pressure (2.140–332 kPa) and CO2 solubility (0.0531–0.8796 mol/mole). The results of the RSM analysis for CO2 solubility indicate that the model demonstrates a strong fit, as evidenced by a Pred-R² of 0.9601, an adjusted R² of 0.9481, and a highly significant F-value of 80.22. The high predicted R² of 0.9601 and 0.9292 values for CO2 solubility and CO2 partial pressure indicate that the predictor variables can explain a substantial amount of the variability in the response variable. The multilayer perceptron (MLP) architecture demonstrated strong correlation capabilities, featuring one hidden layer with 10 and 5 neurons, respectively. Its topology was structured as 4-10-1 for predicting CO2 solubility and 4-5-1 for predicting CO2 partial pressure. The accuracy of the predictions was notably high, with coefficients of determination of 0.99581 for CO2 solubility and 0.99839 for CO2 partial pressure, achieved using the Levenberg-Marquardt algorithm. Upon further analysis, it was concluded that the MLP model exhibited the lowest error rates, with mean square errors of 0.00009085 for CO2 solubility and 0.00316632 for CO2 partial pressure. The findings emphasized that the MLP model not only outperformed the RSM model in accuracy but also demonstrated greater adaptability in handling the intricate variables associated with CO2 solubility and partial pressure in capture technologies.
Published Version
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