Numerical modelling of rotating packed beds used for CO₂ capture processes: A review

Abstract

Over the last decades, renewable and clean energy sources are being rigorously adopted along with carbon capture technologies to tackle the increasing carbon dioxide (CO2) concentration level in the environment. CO2 capture is a quintessential option for tackling global warming issues. In this context, the present paper has reviewed the process intensification equipment called a rotating packed bed (RPB), which is highly industry applicable due to high gravity (HiGee) force. This facilitates strong mass transfer characteristics, a compact design, and low energy consumption. In this review, the current research scenario of RPBs using numerical, computational fluid dynamics (CFD), and mathematical modelling, along with different machine learning approaches in the CO2 capture process, has been reviewed. The different geometry designs, hydrodynamic characteristics, performance parameters, research methods, and their effects on CO2 removal efficiency have been discussed. Furthermore, the latest experimental studies are also summarized, especially in the absorption and adsorption domain. Finally, recommendations have been given to support the RPBs in different industrial and commercial applications of CO2 removal.

Introduction to the K. Nandakumar Festschrift Issue section ofCJCE

“…They verify the effect of different boundary conditions on the particle surface on the wall Nusselt number and also include buoyancy effects. Singh et al [ 6 ] study an intensified process equipment—namely, the rotating packed bed—for CO 2 capture. They perform CFD simulations and develop machine learning models for predicting the hydrodynamic and mass transfer characteristics of the rotating packed bed system.…”

CFD microscale modelling of flow behavior in different parts of a rotating packed bed

“…The k–ε turbulence model has been widely used in micro-scale CFD simulations of RPB by many previous studies 33 , 35 – 39 . This turbulence model has the ability to validate rotating and free shear flow, channel flow, and boundary layer flow with and without pressure gradient 40 .…”

Can Artificial Intelligence Accelerate Fluid Mechanics Research?

“…The supervised approaches rely on labelled data to train the ML algorithm effectively. This is the most common category, with wide applicability in science and technology [4][5][6][7]. Unsupervised ML involves extracting features from high-dimensional data sets without the need for pre-labelled training data.…”