Prediction and optimization of liquid dispersion of monoethanolamine in a rotating packed bed for CO2 absorption

Abstract

Rotating packed bed (RPB) has recently gained traction as a preferred technology for the absorptive removal of CO2. In this device, solvent dispersion is the most crucial hydrodynamic parameter that directly affects the liquid-side mass transfer coefficient. The current study focuses on the optimization of the dispersion of monoethanolamine (MEA), a commonly used CO2 absorption solvent, in the RPB. Computational fluid dynamics (CFD) model was developed and validated against the published experimental data. The validated model was used to investigate the effect of operational parameters such as solvent concentration, rotational speed of the packed bed, inlet velocity, and the contact angle of packing material on MEA dispersion. The liquid dispersion of MEA was modeled using response surface methodology (RSM), artificial neural network (ANN) and adaptive neuro-fuzzy inference systems (ANFIS). The comparison of the modeled data revealed that ANN has the least mean square error (0.16) and is, therefore, the most capable model to predict the liquid dispersion of MEA in the RPB. Based on the ANOVA analysis, the inlet velocity of MEA is found to be an insignificant parameter and, therefore, does not contribute significantly to the liquid dispersion of MEA in the RPB. At optimized operating conditions, the MEA dispersion increased from 5346 to 10485 m−1, thus enhancing the physical absorption of CO2 up to eight times. CFD coupled with ANN for the prediction and RSM for the optimization of liquid dispersion can be employed to optimize industrial-scale RPB for the effective absorptive removal of CO2.