Analysis of effective area and mass transfer in a structure packing column using machine learning and response surface methodology

Abstract

Abstract The study examined mass transfer coefficients in a structured CO2 absorption column using machine learning (ML) and response surface methodology (RSM). Three correlations for the fractional effective area (af), gas phase mass transfer coefficient (kG), and liquid phase mass transfer coefficient (kL) were derived with coefficient of determination (R2) values of 0.9717, 0.9907 and 0.9323, respectively. To develop these correlations, four characteristics of structured packings, including packing surface area (ap), packing corrugation angle (θ), packing channel base (B), and packing crimp height (h), were used. ML used five models, represented as random forest (RF), radial basis function neural network (RBF), multilayer perceptron (MLP), XGB Regressor, and Extra Trees Regressor (ETR), with the best models being radial basis function neural network (RBF) for af (R2 = 0.9813, MSE = 0.00088), RBF for kG (R2 = 0.9933, MSE = 0.00056), and multilayer perceptron (MLP) for kL (R2 = 0.9871, MSE = 0.00089). The channel base had the most impact on af and kL, while crimp height affected kG the most. Although the RSM method produced adequate equations for each output variable with good predictability, the ML method provides superior modeling capabilities.

Keywords

• Mass transfer
• Structured packing
• Response surface methodology
• Machine learning
• CO2 absorption