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. 2025 Feb 28;10(9):8870–8882. doi: 10.1021/acsomega.4c04053

Computational Fluid Dynamic Modeling and Experimental Validation of Adsorption-Based CO2 Separation From Flue Gas Using Porous Activated Carbon

Mansur Aliyu †, Mahmoud M Abdelnaby †,*, Akolade Idris Bakare †, Mahmoud Atef Abdalla †, Hamid Zentou †, Medhat A Nemitallah †,‡,§,*
PMCID: PMC11904686  PMID: 40092767

Abstract

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Both experimental and numerical investigations were performed to examine the impact of vapor on the dynamic adsorption of CO2 in a horizontal fixed column filled with a novel activated carbon material. Experiments were conducted to examine how vapor impacts the CO2 adsorption capacity of the newly developed activated carbon utilizing a DVS machine. Dynamic measurements of the separation of CO2 from CO2/N2 mixtures were conducted under room temperature and atmospheric pressure conditions using a breakthrough experiment. The data obtained from these experiments were then used to validate the numerical model. A parametric numerical study was carried out to investigate the effects of the flow rate, gas temperature, and bed humidity on the CO2 uptake. Results obtained from dynamic vapor sorption (DVS) testing indicate the presence of a constrained adsorption force within the initial monolayer, a characteristic feature reminiscent of a type V isotherm. The numerical findings disclose that the adsorption process efficiency of the synthesized adsorbent is approximately 89%. A 20% increase in the flow rate leads to a 17% decrease in the breakthrough time, and a 20% increase in bed humidity results in an 8% decrease in the breakthrough time due to limited vapor adsorption by the material.

1. Introduction

Emissions of pollutant gases into the atmosphere in the last few decades have been a topic of interest due to their possible environmental influences. Pollutants that influence global warming include, but are not limited to, carbon dioxide (CO2) and nitrogen oxides (NOx), generally termed greenhouse gases. These pollutants are produced when carbon fuels are burnt in the presence of oxidizers (either air or oxygen) in power generation, steel, cement, or transportation industries; therefore, some of these industries contribute a more significant percentage of greenhouse gas emissions.1,2 The continuous increase in CO2 concentration leading to global warming causes and consequent climate change. Some of the climate change effects that are detrimental to the environment include drought, rising sea levels, warming oceans, storms, etc. Thus, a lasting solution must be proposed and implemented for a better environment.

A promising approach to decrease the concentration of CO2 is using CO2 capture and sequestration technology. Different methods for CO2 capture are still in developmental stages: adsorption (either physical or chemical), membrane separation, and exhaust gas condensation. Three options under consideration include precombustion capture (involving the removal of CO2 before combustion, also referred to as the gasification process in power generation plants), postcombustion CO2 capture (involving the removal of CO2 from the exhaust gases), and oxy-combustion (utilizing oxygen instead of air in the combustion process and removing CO2 by condensing the flue gases, primarily composed of CO2 and water vapor).3,4 The postcombustion technologies are built on several chemical and physical processes, including absorption (using alkanol amines), adsorption (using solid sorbents such as carbonate looping mineral carbonation, and/or physisorption), membranes (either gas absorption or separation membranes), and cryogenics (using the differences in desublimation and condensation of flue gases and CO2).1,5,6 Among these technologies, the adsorption process has drawn extensive attention mainly because it requires less energy compared to high energy-consuming alkanol amines (aqueous amine scrubbing) methods.7,8

Various categories of sorbents employed for the capture of CO2 in recent times include metal–organic frameworks (MOFs),9−11 carbonaceous materials,12−14 functionalized porous silica,15−17 zeolites,18−20 metal oxides,21−23 and porous polymers.24,25 It has been reported that MOFs show relatively high capacities for CO2 adsorption of about 8.5 mmol/g26 at a pressure and temperature of 1 bar and 25 °C respectively and a high amount of CO2 uptake of 54.5 mmol/g27 at an elevated pressure of about 50 bar and a temperature of 25 °C. Even with the excellent performance of MOFs in the adsorption process, MOFs exhibit a higher cost than most carbonaceous adsorbents, mainly existing commercially available activated carbons. Also, some MOFs are susceptible to water;28 their porous structure gets damaged when they come in contact with water vapor. These factors limit their applications in some respects, especially in power plants where the exhaust gas contains a significant quantity of water vapor.14 Carbonaceous materials have advantages, including higher thermal stability, higher resistance to water, ease of production, low energy required for regeneration, excellent chemical resistance to acidic and alkaline media, tunable pore structure, and, above all, lower cost. Hence, carbonaceous materials are considered to be highly favorable as adsorbents for CO2 adsorption.

Cazorla-Amorós et al.29 investigated the adsorption of CO2 at temperatures of 0 and 25 °C, spanning a pressure range up to 4000 kPa. This study utilized two sets of activated carbon fibers (ACFs) with varying degrees of burnoff. They reported that at subatmospheric pressures and 0 °C, CO2 adsorption is a proper method for ACF narrow microporosity characterization. They also reported that at a temperature of 25 °C and pressure of about 4,000 kPa, CO2 is adsorbed in the range of supermicroporosity 0.7–2.0 nm pore size. Wickramaratne and Jaroniec14 synthesized a range of activated carbon particles with diameters spanning 200–420 nm and an impressive surface area of up to 2930 m2/g. Their findings revealed exceptionally high CO2 adsorption capacities, measuring 8.05 mmol/g at 273 K and 1 bar pressure and 4.55 mmol/g at 298 K and 1 bar pressure for the prepared activated carbon. Lee and Park30 modified one commercially activated carbon fiber using a chemical activation procedure to achieve exceptional CO2 adsorption uptake. They reported that the chemical activation process of ACFs using KOH improved the specific surface area and the total pore volume by factors of 2.3 and 2.5, respectively, which subsequently led to the highest capacity of adsorbed CO2 of 250 mg/g at a pressure of 1 bar, temperature of 298 K. Wei et al.31 prepared activated carbon from Granular bamboo, which was activated using KOH. They reported that an elevated CO2 adsorption uptake of 7.0 mmol g–1 was measured at a pressure and temperature of 100 kPa and 273 K, respectively. They also suggested that keeping the range of micropore volume between 0.33 and 0.82 nm will yield an excellent linear relationship with the capacity of CO2 adsorption. Yin et al.32 investigated the impacts of activation carbon properties on CO2 adsorption within a pressure range of 0.10–1.00 bar and temperatures 273 and 298 K. They observed that the surface characteristics of activated carbon, particularly oxygen-containing functional groups, had a minimal impact on CO2 adsorption. Conversely, the ultramicropore volume, specifically those less than 0.7 nm, substantially affected the excess CO2 adsorption capacity. This relationship exhibited a linear trend under pressure swing adsorption settings. Singh and Kumar33 conducted experimental measurements of CO2 adsorption isotherms using a commercially accessible activated carbon. The experiments were carried out at pressures ranging from 0 to 45 bar and temperatures of 25, 35, 45, and 65 °C. The obtained data were modeled using two distinct isotherm models: Dubinin–Astakhov and Langmuir. Their findings indicated that the CO2 adsorption process exhibited physisorption characteristics based on the thermodynamic properties derived from the experiments. Given the inherently transient nature of adsorption processes, it is essential to comprehend the dynamic behavior within the packed column. This requires accounting for coupled transport phenomena (mass, momentum, and energy balances), thermodynamics, equilibrium isotherms, adsorption kinetics, and characteristics of the adsorbent to develop an accurate mathematical model. In this context, computational fluid dynamics enables a detailed resolution of transport equations at a differential or ‘cell’ scale, both in space and time.34

The development of postcombustion CO2 adsorption technologies is greatly aided by computational fluid dynamics (CFD) modeling.34,35 It offers insights that are challenging to get through experimental approaches alone by enabling extensive study and simulation of the dynamic behavior of adsorption processes within packed-bed reactors and fixed-bed columns. By optimizing the design and operational parameters, including feed velocity, bed height, and inlet CO2 concentration, CFD models contribute to increased CO2 capture efficiency and effectiveness. Furthermore, by predicting the effects of numerous variables such as temperature, pressure, and humidity on adsorption effectiveness, CFD models enable CO2 capture systems that are more resilient and versatile.36 By combining CFD with experimental data, researchers can better understand adsorption mechanisms and facilitate the scalability and use of postcombustion CO2 adsorption technologies in industrial settings.

In the aspect of computational fluid dynamics (CFD) utilization for CO2 adsorption, Sylvia et al.37 performed CFD analysis to examine the performance of two adsorbents, activated carbon and zeolite 13X, produced from coconut fiber in a fixed bed adsorption column for CO2 removal from a continuous gas flow. ANSYS Fluent code was used, and the influence of bed height and flow rate on the efficiency and adsorption capacity of CO2 removal was investigated by changing the bed heights and feed velocity. They reported that maximum efficiencies of CO2 removal for zeolite 13X and activated carbon adsorbents were achieved at a column height of 10 cm and feed velocity of 50 cm3 /min. The removal efficiencies reported are 63.13 and 57.89% for activated carbon and zeolite 13X adsorbents, respectively. Abdullah and Qasim38 simulated the adsorption of a gas mixture containing CH4/CO2 in a packed-bed reactor containing activated carbon using the CFD method. Simulations were achieved using the ANSYS Fluent code, while a user-defined function (UDF) was developed to compute adsorption properties. No heat transfer occurred inside the bed, and the CO2 concentration and flow rate at the inlet were varied to investigate their effect on the removal efficiency. They reported that as the feed flow rate of the gas increases, the efficiency of the CO2 removal decreases, which was evident from the reduction in the residence time of the feed gas within the bed. They also reported that the inlet CO2 concentration is inversely proportional to the efficiency of the CO2 removal. Qasim et al.39 also noted that the amount of gas adsorbed by the adsorbent (activated carbon) within the column is inversely proportional to the inlet flow rate of the gas mixture. At the same time, the pressure gradient exhibited a proportional relation to the mixture flow rate. Kanellis et al.36 presented CFD simulations of adiabatic fixed-bed adsorption/desorption processes of CO2 on activated carbon. They reveal that the capacity of the activated carbon bed should be fully utilized with a nonadiabatic model before the breakthrough.

After the existing literature on the application of CFD in the context of CO2 adsorption was reviewed, it became evident that there is a need for further research in this field. Some literature offers overly intricate explanations of adsorption processes, while others lack comprehensive details and parametric studies. A recent study conducted by Ramos et al. explained CFD modeling of the adsorption column, however, the study only considered 2-D geometry, and the effect of the bed’s humidity on the adsorption rate was not investigated.34 In this study, the authors introduce different CFD techniques for simulating dynamic CO2 adsorption in a 3-D adsorption column. We thoroughly define all of the parameters related to the activated carbon and the necessary reactions within the ANSYS Fluent environment. The research explores the influence of gas flow rate, bed humidity/water vapor, and gas temperature on CO2 adsorption.

2. Methodology

2.1. Material Synthesis and Experimental Measurements

Carbonization was achieved in a horizontal quartz tubular reactor by using a nitrogen flow. The procedure commenced with 1.0 g of melamine porous polymer, which was heated to 360 °C at a rate of 5 °C per minute and maintained at this temperature for 3.0 h. Subsequently, the materials were subjected to heating at 900 °C at a rate of 3 °C per minute and held at this temperature for an additional 1.0 h. Finally, the material was allowed to cool to room temperature.

Following the synthesis of the material, two series of tests were carried out to evaluate how water vapor impacts the material’s adsorption capacity and CO2 adsorption capacity. The first set employed the dynamic vapor sorption (DVS) method, while the second assessed the material’s CO2 adsorption capacity in a dynamic system using the breakthrough approach.

2.1.1. Dynamic Vapor Sorption (DVS) Technique

The sorption experiments for the CO2, H2O, and CO2/H2O mixture were carried out using the DVS vacuum gravimetric sorption analyzer, as shown in Figure 1, provided by Surface Measurement Systems Ltd. The purpose was to assess how water vapor influences the material’s capacity for adsorbing CO2. This system operates under high vacuum conditions, achieving pressures as low as 10–6 mbar to ensure the thorough outgassing of samples before initiating adsorption experiments. The weight of the samples was measured using the ultra balance from surface measurement systems, which offers a resolution of 0.1 μg. The analyzer can measure complete adsorption and desorption isotherms within a single experiment, accommodating fixed time and mass equilibrium modes. In mass equilibrium mode, the system monitors changes in mass per minute (dm/dt) and progresses to the subsequent partial pressure step once equilibrium is reached. The DVS Vacuum system provides dynamic, semistatic, and static operation modes. In the dynamic mode, sorbate flow is controlled while observing sample mass variations. In the static mode, both upstream and downstream control valves can be closed after sorbate injection. For the sorption experiments conducted in this study, the dynamic mode was selected to maintain the purity of both vapor and gas, replicating real operational conditions.

Figure 1.

Figure 1

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Setup for dynamic vapor sorption experiment.

2.1.2. Dynamic Breakthrough Experiment

The experimental apparatus shown in Figure 2, designed for conducting breakthrough experiments, comprises a horizontal column (referred to as the adsorption bed) measuring 6 cm in length and having an inner diameter of 4 mm and an outer diameter of 6 mm, within which the adsorbent material is placed. This setup was employed to conduct measurements of CO2 adsorption under varying operational conditions.

Figure 2.

Figure 2

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Setup for the breakthrough experiment.

To evaluate the effectiveness of the synthesized activated carbons in capturing CO2 from exhaust gas streams, dynamic breakthrough tests were performed. An activated sample was loaded into the column and exposed to a gaseous mixture of 80% N2 and 20% CO2, closely resembling the composition of a typical flue gas. The gas flow rate was controlled by a flow controller from Bronkhorst, which offered an accuracy of ±0.5% Reading + ± 0.1% Full-Scale. A mixer was used to guarantee a uniform component mixture before entering the adsorption bed. A vacuum pump was also employed to maintain a vacuum environment within the system.

A mass spectrometer positioned at the exit of the adsorption bed was employed to analyze the gas composition. All components of the system, with the exception of the vacuum pump, were linked to a computer (PC) for convenient control and data logging.

2.2. Computational Fluid Dynamics Modeling

The steps involved in the CFD modeling of the dynamic/breakthrough CO2 adsorption process are presented in this section. These steps include discretizing governing equations, geometry development, and geometry meshing, which were achieved using ANSYS. The details of these steps are presented as follows.

2.2.1. Governing Equations

The modified fluid dynamics governing equations for porous media are presented in this section. The unsteady continuity equation for flow through the porous media is given in eq 1.40

2.2.1. 1

where ρg is the density of the gas, Inline graphic is the velocity vector, εc is the column porosity, t is the time, and Sm is the mass source term due to the gas adsorption given in eq 2. Si and Ri are the adsorption rate of i species and the rate of production of i species as a result of reactions, respectively.

2.2.1. 2

The column porosity εc was calculated using eq 3 as follows:

2.2.1. 3

where ρbulk is the density of the bulk, and ρadsorbent is the density of the adsorbent material.

To obtain each species, the Yi local mass fraction, eqs 4 and 5, which represents species transport equations established on diffusion-convection for a given species i are utilized. Each term of eq 3 is significant, and the first term represents a change in concentration of species i, the second term represents diffusion, the third term represents convection and the right side of eq 3 represents the source term.

2.2.1. 4
2.2.1. 5

where ρc is the density of the column, Dm, i is the species’ i coefficient of mass diffusion in the mixture, DT, i is the species’ i coefficient of thermal diffusion, MWi is the species’ i molar mass, qi is the quantity of species i adsorbed, and Yi is the species’ i mass fraction. The wall surface reaction type of species transport is used, and the details of the reactions are presented in Table 1.

Table 1. Details of the Reactions Utilized under the Wall Surface Reaction.
  reactants
 products
 pre-exponential factor activation energy (J/kg mol)
reaction ID species stoichiometry coefficient rate exponent species stoichiometry coefficient rate exponent
1 CO2 1 1 CO2-site 1 0 10,000 0
open-site 1 1
2 CO2-site 1 1 CO2 1 0 10 0
open-site 1 0
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In modeling a porous media, the general momentum equation is modified to contain a sink term, as shown in eq 6.

2.2.1. 6

where p is the static pressure, Inline graphic is the body force, τ̿ is the stress tensor, and Inline graphic is the source term representing the momentum which consists of inertial and viscous loss terms as presented in the first and second right-hand terms of eq 7, respectively.

2.2.1. 7

where C1 is the inertial resistance as a function of column porosity and the packing diameter. α and μ are viscous resistance coefficient and dynamic fluid viscosity, respectively.

The pressure drop across the column is obtained using the popular Ergun Equation. Unlike what has previously been assumed in some literature,39,40 the solid and fluid temperatures in the porous media are observed through a nonequilibrium thermal model, which results in dual meshes creation in the porous zone. For the validation case, it was assumed that the open site was completely covered by vapor because, in the experimental procedure, water vapor was passed through the bed for a specific period.

2.2.2. Geometry

The column is cylindrical and positioned horizontally, with an overall length of 6 cm. The column is divided into three sections: an inlet section, which is 1 cm long; a porous zone, which contains the adsorbent and is 3 cm long; and an outlet section, which is 2 cm long. Flow entered from the left-hand side and exited from the right-hand side during adsorption. A full three-dimensional (3-D) CFD model was established to simulate adsorption. Figure 3 shows a schematic of the 3-D geometry calculation domain and the developed mesh, with the enlargement showing the mesh details and boundary layers.

Figure 3.

Figure 3

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Schematic of the three-dimensional computational domain (a) and the developed mesh (b).

2.2.3. Model Setup and Boundary Conditions

Because of the simplicity of the geometry, a uniform mesh was developed. After comparing experimental data and considering the nano nature of the adsorbent which warrants a very fine mesh to be used, a mesh with 298,587 cells was finally selected. The mesh was deployed into an ANSYS Fluent for CFD simulation. The adsorption type of wall surface reaction was used in species modeling. The activated carbon surface was considered an open site and ready to adsorb CO2; thus, an open site type of adsorption reaction was considered, as presented in Table 1. Tables 2–4 show other details of model formulation, boundary conditions, and the details and operating conditions of the base case.

Table 2. Details of the Model Formulation.
velocity formulation absolute
solver type pressure-based
time transient
pressure-velocity coupling SIMPLE scheme
discretization second order
transient formulation first-order implicit
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Table 4. Operating Conditions of the Validation (i.e., Base) Case.
velocity 0.024 m/s
temperature 300 K
pressure 101,325 Pa
surface-to-volume ratio 1500 m–1
CO2 mass fraction 0.2693
interfacial area density 1500 m–1
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Table 3. Boundary Conditions.
boundary nature of the boundary temperature/thermal species
inlet velocity inlet 300 K CO2 and N2
outlet pressure outlet 300 K (backflow total temperature)  
wall stationary made of steel material adiabatic  
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3. Results and Discussion

3.1. Experimental Results

The DVS technique assessed the solid sorbent affinity toward moisture. This method quantifies the change in the moisture adsorption weight of a sample in response to variations in relative humidity. The moisture adsorption behavior of the microporous activated carbon is contingent on both the relative humidity levels and the presence of polar functional groups on the surface of the activated carbon.41,42 The water isotherm data presented in Figure 4 do not display a steep initial increase in vapor uptake at low relative pressures. This observation suggests a restrained adsorption force within the first monolayer. Such behavior is consistent with a type V isotherm, signifying that the heat of adsorption is only slightly greater than the heat of condensation. This implies a relatively low thermodynamic favorability for adsorption.43

Figure 4.

Figure 4

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DVS data for activated carbon.

Type V isotherms are frequently encountered in substances like activated carbon fibers and carbon molecular sieves.44 Notably, the incorporation of nitrogen doping does not appear to alter the hydrophobic characteristics of activated carbon. Rather, it improves the material’s capacity to capture CO2, suggesting particular enhancement in its adsorption traits.

3.2. Model Validation

The adsorption model was validated by comparing the generated curves with the data obtained from the breakthrough of wet and dry experiments at the outlet point of the column and temperature of 300 K. The wet and dry experiments denote the bed with 100% and 0% humidity, respectively. The validation curves showing the mass fraction of CO2 obtained from the experiments and numerical simulations are presented in Figure 5. It can be observed that the numerical simulations give a good prediction of the breakthrough time, with a deviation of less than 1.5%. For experimental and numerical results, the CO2 mass fraction remains zero at the column outlet until around 70 and 105 s for wet and dry conditions, respectively. In both situations, CO2 is adsorbed until the activated carbon in the porous zone gets saturated at about 70 s for a wet condition and 105 s for a dry condition, after which the CO2 mass fraction starts to be recorded at the outlet point. The processes continued up to the saturation point, where the mass fractions at the inlet and outlet remained the same. Although the time at which saturation is achieved differs, however, the breakthrough time is almost the same as illustrated in Figure 5 which technically marks the end of the process. The breakthrough time is longer under dry conditions due to the absence of water vapor, which results in more active sites being available for CO2 adsorption. Further detailed explanations and analysis are provided in subsequent sections.

Figure 5.

Figure 5

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Breakthrough curve obtained from experiments and numerical simulations.

Figures 6–8 show the contour plots of CO2 concentrations in the flow, the open site surface coverage, and the CO2 surface coverage, respectively for the wet condition, at different flow times and the conditions given in Table 4. These figures give more insight into the details of the model used. The section of the open site and the CO2 surface coverage represent the porous zone containing the adsorbent where the adsorption process is taking place, as indicated. Looking at these figures, it can be observed that as the adsorption process is going on and as the flow proceeds downstream of the column as indicated by the CO2 mass concentration in Figure 6, the surface coverage of the open site reduces while the surface coverage of the CO2 increases as shown in Figures 7 and 8, respectively. This indicates that the adsorption process is taking place properly. At any point in time, the summation of surface coverages (open site and CO2) results in 50% (i.e., 0.5) of the entire surface, because the bed (i.e., activated carbon) is completely humidified. Figures 7 and 8 also show that the efficiency of the process is around 89%, which is explained by the nonzero value of the open site surface coverage even at 100 s after the breakthrough (i.e., Figure 7) thus, the CO2 does not completely cover the surface of the activated carbon.

Figure 6.

Figure 6

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Contour plots of CO2 concentration at different flow times for the base case (i.e., wet condition).

Figure 8.

Figure 8

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Contour plots of the CO2 site coverage at different flow times for the base case.

Figure 7.

Figure 7

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Contour plots of open site coverage at different flow times for the base case.

Figure 9 shows the velocity contours at different flow times. It can be seen that the velocity distribution varies with time and differs from one section to another. The minimum value is recorded within the porous zone, while the maximum value occurs at the outlet section. The minimum value of velocity in the porous section indicates the presence of adsorbent, which retards the flow. As the process progresses over time, the velocity changes in both the inlet and outlet sections of the column while remaining constant at the porous zone. Nonslip condition is observed except at the porous zone where both solid (adsorbent) and fluid exist. Figure 10 shows the temperature contours at different flow times. It can be observed that there is no significant change in the temperature during the adsorption process.

Figure 9.

Figure 9

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Contour plots of velocity distribution at different flow times for the base case.

Figure 10.

Figure 10

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Contour plots of the temperature distribution at different flow times for the base case.

3.3. Effect of Flow Rate on CO2 Uptake

A parametric study was conducted where some parameters were varied to see their effects on breakthroughs. The parameters varied, including feed flow rate, flue gas temperature, and bed humidity. Figure 11 shows the effect of flow rate on the breakthrough at a temperature of 300 K and at the set condition of the base case/wet condition (Table 4). The base case in Figure 11 has the same condition as that of the validation case but with different flow rates. The flow rate varied on an incremental level but at the same CO2 concentration at the inlet. It can be observed that the adsorption capacity decreases as the flow rate increases, which results in a smaller breakthrough mass transfer zone. This scenario is expected because as the flow rate increases, the interaction time between the gas and the adsorbent becomes shorter which leads to a reduction in the efficiency of CO2 removal. Thus, the higher the flow rate, the lower the gas retention time within the column. Similar results have been reported in the literature.38 Further explanation can be found in Figures 12 and 13 which show the contour plots of CO2 concentrations and CO2 site coverage at different flow rates and flow times of 20 s, respectively. It can be seen from these two figures (i.e., 12 and 13) that at a given time increasing the flow rate results in the advancement of CO2 concentration downstream of the column due to a decrease in retention time, which subsequently increases the CO2 site coverage. Typically, a 20% rise in the flow rate leads to a 17% reduction in the breakthrough time.

Figure 11.

Figure 11

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Effects of the flow rate on CO2 uptake at the set condition of the base case.

Figure 12.

Figure 12

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Contour plots of CO2 concentration at different flow rates and flow time of 20 s.

Figure 13.

Figure 13

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Contour plots of CO2 site coverage at different flow rates and flow time of 20 s.

3.4. Effect of Flue Gas Temperature on CO2 Uptake

Figure 14 shows the effect of the gas temperature on the breakthrough. Similarly, the base case (wet condition) represents the validation case at the test temperature of 300 K. It can be observed that as the gas temperature increases, the residence time increases, resulting in a bigger breakthrough mass transfer zone. Unlike the flow increments, increasing the gas temperature increases the adsorption capacity and hence improves the efficiency of CO2 removal. This happens because, as the gas temperature increases, the bed gets heated up, and the vapor breaks away from the activated carbon surface, increasing the open site’s surface coverage for CO2 to occupy. Furthermore, due to the limited adsorption force observed within the first monolayer, as described in the previous DVS experiment in Section 3.1, the activated carbon readily releases the vapor, thus creating more active sites for CO2. This, in turn, extends the breakthrough time. Figures 15 and 16 show the contour plots of CO2 concentrations and CO2 site coverage at different temperatures and a flow time of 20 s. A close look at these figures shows that the CO2 mass fraction and CO2 site coverage are higher in the base case at a temperature of 300 K and decrease marginally as the temperature increases. Hence, increasing the gas temperature increases the residence time, which results in a larger breakthrough mass transfer area.

Figure 14.

Figure 14

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Effect of temperature on CO2 uptake at the set conditions of the base case.

Figure 15.

Figure 15

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Contour plots of CO2 concentration at different temperatures and flow time of 20 s.

Figure 16.

Figure 16

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Contour plots of CO2 site coverage at different temperatures and flow time of 20 s.

3.5. Effect of Bed Humidity on CO2 Uptake

Due to the huge variety of adsorbents, water plays several distinct roles, from a serious inhibitor of CO2 adsorption to an exceptional promoter. Water may decrease the rate of CO2 adsorption or have a reverse effect. For adsorbents containing amine, water is essential for their stability.7 Thus, investigating the effect of the presence of vapor on the adsorbent surface is important. Results show that having a dried bed seems to improve the adsorption capacity and removal efficiency. This is evident from Figure 17, which shows the effect of the bed’s humidity or vapor variation on the breakthrough. The base case also represents the validation case with a vapor site coverage of 100%. As the vapor site coverage is reduced, the gas retention increases, leading to a larger breakthrough mass transfer zone. When the bed is less humid, the open site increases, allowing more CO2 to be adsorbed by activated carbon. Figures 18 and 19 show the contour plots of CO2 concentrations and CO2 site coverage at different beds’ vapor coverage/humidity and flow time of 20 s, respectively. The figures show that increasing the bed’s humidity decreases the residence time, thereby reducing the amount of CO2 captured. It can be observed from Figure 18 that at a vapor coverage of 50%, more CO2 is adsorbed as compared to 100% vapor coverage, which is the situation in the base case. Generally, a 20% increase in bed humidity results in an 8% decrease in the breakthrough time due to limited vapor adsorption by the material. Hence, a dried bed tends to adsorb more CO2 than a humid bed.

Figure 17.

Figure 17

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Effects of bed’s vapor coverage/humidity on CO2 uptake at the set condition of the base case.

Figure 18.

Figure 18

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Contour plots of the CO2 concentration at different beds’ vapor coverage/humidity and flow time of 20 s.

Figure 19.

Figure 19

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Contour plots of CO2 site coverage at different beds’ vapor coverage/humidity and flow time of 20 s.

4. Conclusions

The study presented both experimental studies and simplified numerical investigations to examine the impact of water vapor, gas flow rate, and temperature on dynamic CO2 adsorption in a horizontal fixed column containing activated carbon. The numerical study was achieved using ANSYS Fluent. Transient simulations were conducted by considering the three-dimensional geometry of the adsorption column. The porous section of the column was modeled by using the porous media approach and nonequilibrium model. A wall surface reaction type of species transport model was used in the porous zone. The results of dynamic vapor sorption (DVS) testing show the presence of a limited adsorption force within the first monolayer, which is indicative of a type V isotherm. This finding implies that the heat associated with adsorption is only somewhat more than condensation’s. Moreover, dynamic CO2 adsorption studies show that the ANSYS Fluent software can accurately estimate the breakthrough mass transfer zone during dynamic CO2 adsorption on activated carbon adsorbents. The expected findings differ from the experimental data by less than 1.5%. Numerical results show that the efficiency of the adsorption process using the synthesized adsorbent is around 89%. Increased gas flow rate and bed humidity tend to lower the extent of the breakthrough mass transfer zone, while elevated temperature increases its extent. A 20% increase in flow rate results in a 17% reduction in breakthrough time, while a 20% increase in bed humidity results in an 8% reduction in breakthrough time due to limited vapor adsorption by the material.

Acknowledgments

The authors appreciate the support received from the King Fahd University of Petroleum and Minerals (KFUPM) to perform this work through the Hydrogen Consortium under project number H2FC2315. The partial support received through the KFUPM Interdisciplinary Research Center for Hydrogen and Energy Storage (IRC-HES) under project number INHE2308 is also appreciated.

The authors declare no competing financial interest.

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