DOI: 10.3303/CET23103140 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 12 April 2023; Revised: 28 May 2023; Accepted: 30 June 2023 
Please cite this article as: Kantouros B., Kazepidis P., Papadopoulos A.I., Seferlis P., 2023, Investigating CO2 Absorption in Rotating Packed 
Beds: Development, Validation, and Operational Analysis of a Rate-Based Model, Chemical Engineering Transactions, 103, 835-840  
DOI:10.3303/CET23103140 
  

 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 103, 2023 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.cetjournal.it 

Guest Editors: Petar S. Varbanov, Panos Seferlis, Yee Van Fan, Athanasios Papadopoulos 

Copyright © 2023, AIDIC Servizi S.r.l. 

ISBN 979-12-81206-02-1; ISSN 2283-9216 

Investigating CO2 Absorption in Rotating Packed Beds: 

Development, Validation, and Operational Analysis of a Rate-

Based Model 

Babis Kantourosa, Panagiotis Kazepidisb, Athanasios I. Papadopoulosb, Panos 

Seferlisa,* 

aDepartment of Mechanical Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece  

bChemical Process and Energy Resources Institute, Centre for Research and Technology-Hellas (CERTH), Thermi 

Thessaloniki 57001, Greece 
 seferlis@auth.gr 

Effective modelling of the Rotating Packed Bed (RPB) process is crucial for accurately simulating CO2 

absorption in aqueous amine solutions. Existing models often incorporate simplifying assumptions and rarely 

capture the simultaneous variations of critical phenomena inside the RPB apparatus, such as gas phase CO2 

mass flow rate, liquid and gas temperatures, and pressure drop. This work addresses these challenges by 

developing a rate-based, steady-state model designed for CO2 capture, using monoethanolamine (MEA) as the 

solvent. Unlike previous works, this model effectively captures all these critical phenomena simultaneously. The 

model employs a first principles approach and solves material and energy balance equations using the shooting 

method. Two-film theory is used to represent mass transfer in conjunction with up-to-date correlations. The 

predicted results exhibit excellent agreement with the experimental data for a 30 wt. % MEA solution when 

comparing rich loading, with a maximum deviation of 1.1 %. The analysis of previously overlooked phenomena 

in RPB-based CO2 absorption provides valuable insights regarding the variations of key parameters along the 

radius, such as the influence of pressure drop and temperature. The observed decrease in gas pressure and 

sharp drop near the inner radius demonstrate the significant impact of frictional losses. In contrast, the 

temperature profiles of the gas and liquid phase reveal the interplay between exothermic reactions and counter-

current flow.  

1. Introduction 

Post-combustion CO2 capture absorption systems are receiving increased attention to mitigate greenhouse gas 

emissions. Rotating Packed Bed (RPB) units and reactive solvents have demonstrated significant potential for 

improving CO2 capture efficiency (Borhani et al., 2018). Process intensification using RPBs is a complex 

procedure due to the simultaneous phenomena that occur between the gas and liquid phases. Accurate 

modelling of this process is considered a significant challenge, as it can serve as a prediction and design tool 

for different operating conditions (Dimoliani et al., 2021). Research studies involving custom-made models for 

CO2 capture have often employed simplifying assumptions concerning the absorption process. While these 

assumptions may be adequate for studying small RPB units, they could lead to significant discrepancies when 

applied to scaled-up operations (Hendry et al., 2020). For instance, Qian et al. (2009) developed a mathematical 

model to describe CO2 absorption using an aqueous N-methyldiethanolamine (MDEA) solution. However, their 

model assumed isothermal operation, overlooked the pressure drop along the radius, and omitted the presence 

of water in the gas phase. Similarly, Yi et al. (2009) conducted an analysis using as a solvent the Benfield 

solution (hot potassium carbonate promoted by diethanolamine). Their model neglected the presence of water 

in the gas phase and the pressure drop while assuming isothermal absorption. Otitoju et al. (2023) reviewed 

existing models for MEA-based RPB absorbers that utilise the two-film theory for mass transfer. Commercial 

tools such as Αspen Plus® or gPROMS® were primarily used. The developed models focused on the effects of 

key operational parameters, such as rotational speed, lean MEA solution concentration and temperature and 

835



CO2 capture efficiency. However, these studies provided limited results concerning the depiction of phenomena 

occurring across the radial direction of the RPB. Joel et al. (2014) presented the liquid temperature profile along 

the radius in their model, while Borhani et al. (2018) focused on the CO2 mole fraction profile in the gas phase. 

Luo et al. (2021) expanded the modelling parameters to include the CO2 absorption profile along the radius, 

alongside liquid and gas temperatures, using the surface renewal theory. Their analysis did not include the 

pressure drop profile. 

The objective of this work is to develop and validate a first principles model for CO2 absorption in RPBs that 

employs an aqueous MEA solution. The model uses the two-film theory to represent the mass transfer process 

and integrates a broader range of phenomena occurring along the RPB radius than what is typically considered 

in existing models. Such phenomena include variations in gas phase pressure, temperature, and CO2 mass flow 

rate. The model includes all the necessary compounds (CO2, H2O, MEA) in both the gas and liquid phases. The 

mass transfer coefficients are based on comparisons between a variety of mass transfer correlations and 

experimental data (Oko et al., 2019). Counter-current flow is implemented to augment mass transfer compared 

to co-current and cross-flow configurations (Kolawole et al., 2018). Figure 1 illustrates the gas flowing from the 

outer section towards the inner section while the liquid flows in the opposite direction. Water at the gas inlet is 

accounted for to mimic realistic operating conditions during the absorption process. 

 

Figure 1: Counter-Current Flow in a Rotating Packed Bed absorption process 

2. Methodology 

A rate-based approach is essential to accurately describe the absorption process, primarily due to the increased 

rates of mass and heat transfer. Material and energy balance equations incorporate steady-state conditions and 

one-dimensional fluid flow in the radial direction. The gas phase is considered ideal and consists of CO2, H2O, 

N2, and MEA. The mass transfer of N2 can be ignored since it is an inert gas. Chemical reactions occur 

exclusively in the liquid film and are quantified by the enhancement factor.  

2.1 Model development 

In this work, the material and energy balance equations are derived based on the work of Kang et al. (2014), as 

presented in equations Eq(1) to Eq(4). The equation for pressure drop (Eq(5)) is incorporated, as its significant 

role in the absorption process has been highlighted by Hendry et al. (2020). These equations along with the 

corresponding boundary conditions are presented as follows: 

Material Balance for liquid phase:  

𝑑(𝐹𝑙𝑥𝑖)

𝑑𝑟
= 𝜀𝑎𝑔𝑙𝐴𝑐𝑁𝑖  ,      𝐵𝐶: 𝑥𝑖 = 𝑥0 𝑎𝑡 𝑟 = 𝑟𝑖𝑛𝑛𝑒𝑟 ,

𝑑(𝐹𝑙𝑥𝑖)

𝑑𝑟
= 0 𝑎𝑡 𝑟 =  𝑟𝑜𝑢𝑡𝑒𝑟    (1) 

Energy Balance for liquid phase: 

𝑑(𝐹𝑙𝐶𝑝𝑙𝑇𝑙)

𝑑𝑟
= 𝜀𝑎𝑔𝑙𝐴𝑐(ℎ𝑔𝑙(𝑇𝑙 − 𝑇𝑔) − 𝛥𝐻𝑟𝑥𝑛𝑁𝐶𝑂2

− 𝛥𝐻𝑣𝑎𝑝𝑁𝐻2𝑂),      𝐵𝐶:  𝑇𝑙 = 𝑇𝑙0
 𝑎𝑡 𝑟 = 𝑟𝑖𝑛𝑛𝑒𝑟 ,

𝑑(𝐹𝑙𝐶𝑝𝑙𝑇𝑙)

𝑑𝑟
=

0 𝑎𝑡 𝑟 = 𝑟𝑜𝑢𝑡𝑒𝑟   
(2) 

Material Balance for gas phase: 

𝑑(𝐹𝑔𝑦𝑖)

𝑑𝑟
= 𝜀𝑎𝑔𝑙𝐴𝑐𝑁𝑖  ,      𝐵𝐶: 𝑦𝑖𝑛𝑛𝑒𝑟 = 𝑦0 𝑎𝑡 𝑟 = 𝑟𝑜𝑢𝑡𝑒𝑟 ,

𝑑(𝐹𝑔𝑦𝑖)

𝑑𝑟
= 0 𝑎𝑡 𝑟 = 𝑟𝑖𝑛𝑛𝑒𝑟       (3) 

Energy Balance for gas phase: 

𝑑(𝐹𝑔𝐶𝑝𝑔𝑇𝑔)

𝑑𝑟
= 𝜀𝑎𝑔𝑙ℎ𝑔𝑙𝐴𝑐(𝑇𝑙 − 𝑇𝑔) ,      𝐵𝐶: 𝑇𝑔 = 𝑇𝑔0 𝑎𝑡 𝑟 = 𝑟𝑜𝑢𝑡𝑒𝑟 ,

𝑑(𝐹𝑔𝐶𝑝𝑔𝑇𝑔)

𝑑𝑟
= 0 𝑎𝑡 𝑟 = 𝑟𝑖𝑛𝑛𝑒𝑟    (4) 

836



Pressure Drop for gas phase: 

𝑑𝑃

𝑑𝑟
= 𝜌𝑔𝜔2𝑟 + 𝜌𝑔𝑢𝑔

𝑑𝑢

𝑑𝑟
+

150(1 − 𝜀𝑔)2𝜇𝑔

𝑑𝑝
2𝜀𝑔

3
𝑢𝑔 +

1.75(1 − 𝜀𝑔)𝜌𝑔

𝑑𝑝𝜀𝑔
3 𝑢𝑔 2  ,    𝐵𝐶: 𝑃 = 𝑃0 𝑎𝑡 𝑟 = 𝑟𝑜𝑢𝑡𝑒𝑟  ,  

𝑑𝑃

𝑑𝑟
= 0 𝑎𝑡 𝑟 = 𝑟𝑖𝑛𝑛𝑒𝑟 

(5) 

The equations encapsulate molar flow balances for each component 𝑖 along the radius 𝑟 (m), represented by 

the derivative terms. These derivative terms contain various variables specific to the liquid phase such as the 

molar flow rate 𝐹𝑙 (mol/s), the mole fraction of each component 𝑥𝑖 (-), the temperature 𝑇𝑙 (∘C) and the specific 

heat capacity 𝐶𝑝𝑙 (J/mol∘C). For the gas phase the derivative terms include variables such as the molar flow 𝐹𝑔 

(mol/s), the mole fraction of each component 𝑦𝑖 (-), the temperature 𝑇𝑔 (∘C), the specific heat capacity 𝐶𝑝𝑔 

(J/mol∘C) and the gas pressure 𝑃 (kPa). In the boundary conditions the symbols 𝑥0, 𝑇𝑙0, 𝑦0, 𝑇𝑔0 and 𝑃0 represent 

the inlet conditions of each respective phase. The equations also include 𝐴𝑐 = 2𝜋𝑟𝑧  (m2) representing the 

tangential area, the void fraction of packing 𝜀 (-), the gas-liquid interfacial area 𝑎𝑔𝑙 (m
2/m3), and the axial length 

𝑧 (m). Energy balance equations encompass interfacial heat transfer, the exothermic reaction of CO2 in the 

liquid phase, and the process of water vaporization or condensation. Specifically, the value of heat transfer 

interfacial coefficient ℎgl (W/m2∘C) is determined based on thermophysical properties. `The reaction enthalpy 

Δ𝐻𝑟𝑥𝑛 of CO2 (J/mol) is calculated as a function of liquid temperature and loading (mol CO2 /mol MEA) using the 

correlation provided by Luo et al. (2021). The heat of vaporization Δ𝐻vap (J/mol) is computed as a function of 

temperature. The pressure drop equation includes the effects of centrifugal acceleration, the radial acceleration 

effect from Bernoulli's principle, and frictional losses inside the packing. Frictional losses are incorporated in 

Eq(5) using the Ergun equation (Hendry et al., 2020). Variables relevant to the pressure drop equation 

encompass gas thermophysical properties like density 𝜌𝑔 (kg/m3) and viscosity 𝜇g (Pas), the angular speed 

𝜔 (rad/s), the superficial gas velocity 𝑢𝑔 (m/s), the gas hold-up 𝜀g (-), and 𝑑𝑝 (m), denoting the spherical 

equivalent particle diameter. 

2.2 Two-Film theory 

The molar transfer flux, 𝑁𝑖 (mol/m2s), is calculated for each molecular component (CO2, H2O, MEA) based on 

two-film theory as described by Kvamsdal et al. (2009) in Eq(6): 

𝑁𝑖 = 𝐾𝑔,𝑖(𝑃𝑔,𝑖 − 𝑃𝑖
∗) (6) 

where 𝑃𝑔,𝑖 (Pa) is the partial pressure of bulk gas phase, 𝑃𝑖
∗ (Pa) is the equilibrium gas partial pressure and 𝐾𝑔,𝑖 

(mol/m2 Pa s) is the overall mass transfer coefficient as follows: 

1

𝐾𝑔,𝑖
=

𝑅𝑔𝑇𝑔

𝑘𝑔,𝑖
+

𝐻𝑒𝑙,𝑖

𝐸𝑖𝑘𝑙,𝑖
 (7) 

The right-hand side terms of Eq(7) represent the gas phase resistance and liquid phase resistance. The liquid 

phase resistance for MEA and H2O is considered negligible. Henry’s constant 𝐻𝑒𝑙,𝑖  (Pa m3/mol) for the solubility 

of CO2 in aqueous MEA solution is determined using the expression described in Restrepo et al. (2015). The 

enhancement factor is employed in the mass transfer model to account for the reaction rate of CO2 with MEA, 

as illustrated in Eq(8): 

𝐸CO2
=

√𝑘𝑎𝑝𝑝𝐷𝑙,CO2

𝑘𝑙,CO2

 (8) 

where 𝐷𝑙,CO2
 (m2/s) is the liquid-phase diffusivity of CO2, 𝑘𝑎𝑝𝑝(1/s) is the apparent reaction rate constant. The 

latter is derived from the overall reaction between CO2 and MEA, adhering to the termolecular mechanism 

(Aboudheir et al., 2003). The mass transfer model necessitates reliable correlations to accurately predict the 

effective interfacial area and the mass transfer coefficients for both liquid and gas films. The scarcity of 

experimental data for mass transfer in RPBs underscores the challenges of modelling this process. This study 

applies the correlation for the gas film mass transfer coefficient, 𝑘𝑔,𝑖 (m/s), provided by Chen et al. (2011), which 

is based on experimental data for 𝐾𝐺𝑎  (1/s), related to an RPB. The liquid film mass transfer coefficient, 𝑘l,𝑖 

(m/s), as proposed by Tung and Mah (1985), offers superior accuracy in comparison to more complex 

correlations. The effective interfacial area (Billet and Schultes, 1999) is employed because of its ability to 

consider the impact of rotational speed accurately. Thermophysical properties, crucial for the calculation of mass 

transfer coefficients in both liquid and gas phases, are solely derived from literature in this study, specifically 

837



employing correlations from the works of Borhani et al. (2018). The calculation of equilibrium component partial 

pressures in Vapor-Liquid Equilibrium (VLE) data critically affects the mass transfer rate, notably for CO2. These 

pressures are estimated in line with the work of Damartzis et al. (2014) for a 30 wt. % MEA solution, adopting 

polynomial approximations in relation to loading and temperature. The shooting method (Yi et al., 2009) serves 

to solve the BVP as shown in Eq(1) to Eq(5). The BVP is converted into an Initial Value Problem (IVP) by 

assuming gas boundary conditions at the inner radius and progressing from the inner to the outer radius. By 

iteratively adjusting the initial boundary condition guess, the solution was fine-tuned until reaching the requisite 

level of precision. 

3. Results and discussion 

3.1 Model validation 

The developed model is validated using steady-state experimental data for aqueous MEA solution from Jassim 

(2002). The pilot-plant absorber had an inner diameter of 0.156 (m), outer diameter of 0.396 (m) and axial length 

of 0.025 (m). The RPB contained an expanded stainless-steel mesh which had a void fraction of 0.76 and 

specific surface area of 2132 (m2/m3). The experimental conditions are listed in Table 1. 

Table 1: Experimental Conditions (Jassim, 2002)- RPB Absorber  

Case Run w/w  

MEA  

(%) 

Rotor 

speed 

(rpm) 

Lean  

flow  

(kg/ s) 

Lean  

temperature  

(oC) 

Lean 

loading 

(-) 

Vapor 

CO2  

(vol %) 

Vapor 

H2O 

(vol %) 

Vapor  

N2 

(vol %) 

Gas 

 flow 

(kmol/h) 

1 32 600 0.66 40.5 0.3349 0.0449 16.9 78.5 2.87 

2 30 1,000 0.66 40.6 0.3374 0.0433 17.05 78.5 2.87 

3 31 600 0.35 39.9 0.3254 0.0438 17.03 78.5 2.87 

4 31 1,000 0.35 39.3 0.3282 0.0361 17.41 78.5 2.87 

In the dataset of Jassim (2002) the CO2 concentration at the inlet was 4 mol %, and the initial CO2 loading in 

MEA was approximately 0.3 (mol CO2/mol MEA). The validation of the intensified absorber model was 

performed by assessing rich loading.  

Table 2: Key Variable Validation, RD stands for relative deviation 

Case Experimental Loading (mol CO2/ mol amine) Predicted Loading (mol CO2/ mol amine) RD (%) 

1 0.3378 0.3416 1.1 

2 0.3411 0.3443 0.9 

3 0.3342 0.3372 0.9 

4 0.3310 0.3382 0.2 

In Table 2, the model demonstrates a significant level of agreement with published data for rich loading. 

Specifically, the maximum relative deviation for rich loading does not exceed 1.1 (%). These findings highlight 

the accuracy and reliability of the MEA model in simulating CO2 absorption processes. 

3.2 Operational analysis  

The operational analysis is presented here that includes for the first time results for phenomena that were 

investigated separately in previous works, due to omission of the underlying representations. Figures 2 and 3 

present the radial profiles for gas phase CO2 mass flow rate, gas pressure drop, and liquid and gas 

temperatures. Realistic operating conditions for CO2 capture applications required certain alterations from the 

conditions used for validation. The experimental setup of Jassim (2002) included a liquid-to-gas ratio, L/G 

(kg/kg), of up to 25, a level that could potentially lead to flooding. An L/G ratio of 3.4 was chosen for a closer 

approximation of a standard MEA absorption system, coupled with a rotational speed of 300 (rpm) (Qammar et 

al., 2018). The feed gas was based on Kazepidis et al. (2021). Details are shown in Table 3. 

Table 3: RPB Simulation Conditions 

Initial Conditions    

Gas flow in absorber (mol/s) 320 MEA weight percentage (w/w %) 30 

CO2 composition in flue gas (v/v %) 14.11 Lean loading (mol CO2/ mol amine) 0.2 

Gas inlet temperature (oC) 25 Liquid temperature (oC) 40 

L/G (kg/kg) 3.4 N (rpm) 300 

838



The presentation approach of the following figures allows for a clear examination of the behaviour of each 

variable within different segments of the RPB radius, which is the main focus of the analysis. The results are 

presented in relation to the normalized radius from the inner to the outer section of the RPB. 

 
(a) 

 
(b) 

Figure 2: a) Gas mass flow rate of CO2 profile, b) Gas pressure profile 

Figure 2a reveals a steep gradient near the outer radius, the point that the feed gas is introduced into the RPB. 

This gradient is explained by the increased partial pressure of the CO2 gas and the maximum tangential area. 

As the gas flows toward the inner radius, the gradient gradually stabilized until it reached a plateau. Figure 2b 

depicts a steady decrease in gas pressure across the majority of the radius, until a sharp drop occurs upon the 

inner radius. This phenomenon is primarily a consequence of frictional losses within the packing material. In 

particular, an increase in viscosity of the fluid is combined with a rise in superficial gas velocity. The latter is a 

result of the limited available tangential area. Together, these factors mainly contribute to an overall increase in 

frictional losses. Figure 3a reveals the temperature profile of the liquid, which reflects the trend of the CO2 

absorption profile shown in Figure 2a. The increase in liquid phase temperature, driven by the exothermic 

reaction between MEA and CO2, leads to heat transfer and influences the variation in gas temperature, Figure 

3b. It is worth noting that the liquid phase dominates the overall behaviour of the system. This interplay is a 

consequence of the counter-current flow, facilitating efficient heat exchange between the phases. Specifically, 

the heat transfer rate in the gas phase is significantly dependent on an accurate estimation of the interfacial 

heat transfer coefficient. 

 
(a) 

 
(b) 

Figure 3: a) Liquid temperature profile, b) Gas temperature profile 

4. Conclusions 

A custom-made model was developed to simulate all critical phenomena during solvent-based CO2 capture in 

a Rotating Packed Bed (RPB) unit. The model was validated against experimental data and found to exhibit 

excellent agreement, using monoethanolamine (MEA) as the solvent. Utilizing a rate-based approach under 

steady-state conditions, the model applied the two-film theory for the simulation of mass transfer. This work also 

presented comprehensive process diagrams that provide unique insights into the combined phenomena 

occurring along the RPB radius. The simulation revealed distinct patterns in gas pressure, temperature profiles, 

and CO2 mass flow rate inside the RPB. Future work will involve applying this model to scale-up studies, while 

exploring its use with other solvents. This will provide a foundation for more reliable and comparative analysis 

839



for intensified CO2 absorption. In conclusion, precise process modelling in Rotating Packed Beds is crucial for 

further advancements in this field. 

Acknowledgments 

Funded by the European Union. This project has received funding from the European Union's Horizon Europe 

research and innovation programme under grant agreement No. 101075727. Views and opinions expressed are 

however those of the author(s) only and do not necessarily reflect those of the European Union or European 

Climate, Infrastructure and Environment Executive Agency (CINEA). Neither the European Union nor the 

granting authority can be held responsible for them. 

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