Insight into the experimental and modeling study of process intensification for post-combustion CO2 capture by rotating packed bed

Abstract

The purpose of rotating packed bed is to intensify process conditions by using centrifugal forces. The effective interfacial area is a critical design factor and has a direct relationship with operational condition and mass transfer rate. Process intensification by the rotating packed bed is an emerging technology to improve the mass transfer rate in a high gravity system. Since there are limited modeling studies in order to control rotating packed bed parameters, in the present study, the multilayer perceptron artificial neural network (MLP) framework was successfully used to investigate the gas-liquid effective interfacial area in a rotating packed bed. In this regard, a number of 265 experimental data for the gas-liquid effective interfacial area was utilized by considering three groups including operational factors, physical dimension, and gas-liquid properties as the network’ inputs. The mean relative error and R-square as analogy factors for verification of the model accuracy obtained to be 8.2% and 0.97, respectively. Accordingly, the present model can be a huge value in the CO2-liquid system and it is introduced as a novel strategy to determine the gas-liquid effective interfacial area in a rotating packed bed.