Mass transfer performance of CO2 capture in rotating packed bed: Dimensionless modeling and intelligent prediction

Rotating packed beds have been demonstrated to be able to intensify the physicochemical process of multiphase transportation and reaction in the fields of energy and environment, and successfully applied in the field of CO2 emission control. However, modeling and prediction of gas–liquid mass transfer especially for mass transfer with chemical reaction are rare due to the complexity of multiphase fluid flow and transportation. In view of the inaccuracy of semi-empirical models and the complexity of computational fluid dynamics models, an intelligent correlation model was developed in this work to predict the mass transfer coefficient more accurately for CO2 capture with NaOH solution in different type rotating packed beds. This model used dimensional analysis to determine the independent variables affecting the mass transfer coefficients, and then used least squares support vector regression (LSSVR) for prediction. An optimized radial basis function was obtained as kernel function based on grid search coupled with simulated annealing (SA) and 10-fold cross-validation (CV) algorithms. The proposed model had the mean square error of 0.0016 for training set and 0.0012 for testing set. Compared with the models based on multiple nonlinear regression (MNR) and artificial neural network (ANN), the present model decreased mean squared error by 91.06% and 38.46% for training set and 94.57% and 53.85% for testing set respectively, suggesting it had superior performance on prediction accuracy and generalization ability.