Multivariate Data Analysis for Parameters Effect on CO2 Removal Efficiency

In this paper, both the main effects and interaction effects of parameters on CO 2 removal efficiency were investigated. Flue gas stream data from a 500MW coal power plant has been used for the model development. The complete removal process is implemented in Aspen Plus with selected operating conditions and parameters using Monoethanolamine as solvent. The base case model is developed in Aspen Plus with specific parameter values to achieve 85% removal efficiency. The CO 2 removal efficiency variation with different parameters; such as number of stages, inlet solvent flow rate, lean loading, temperature of the flue gas and solvent stream, absorber packing height and diameter and absorber pressure are considered as the most important parameters for sensitivity analyses. The data collected from simulations were analysed using Principal Component Analysis, Principal Component Regression and Partial Least Square-regression. The correlation between variables were studied, which indicate that inlet solvent flow rate, absorber packing height and diameter, absorber pressure and temperature of the solvent stream are positively correlated with CO 2 removal efficiency whereas the lean loading and temperature of flue gas are negatively correlated with efficiency.