Analysis of Mass Transfer Performance of Monoethanolamine-Based CO2 Absorption in a Packed Column Using Artificial Neural Networks

Abstract

Exact and reliable estimation of mass transfer performance is very important for the design, simulation, and optimization of CO2 absorption in a packed column. In this study, two types of artificial neural networks (ANNs), namely back-propagation neural network and radial basis function network, were applied to predict the mass-transfer performance of CO2 absorption into aqueous monoethanolamine (MEA) in packed columns (containing Berl saddles, Pall rings, IMTP random packing, and 4A Gempack, Sulzer DX structured packing, respectively) from input variables. These variables were inert gas flow rate, liquid flow rate, solution concentration, liquid CO2 loading, CO2 mole fraction, temperature, and total packing area, which were considered to predict the targeted output mass transfer variables. The predicted results from ANN were validated against experimental data as well as compared with results from well-known correlations in terms of the volumetric mass flux, CO2 mole fraction, and temperature profiles along the height of the packed column. The comparisons between the predicted and experimental results showed that the proposed ANN models performed very well in predicting mass transfer performance of CO2 absorption into aqueous MEA in a packed column.