《1.1. Background》

1.1. Background

Increasing concentrations of  greenhouse  gases  (GHGs)  caused by anthropogenic activities are responsible for most of global warming [1]. Carbon dioxide (CO2) is a main GHG, accounting for 76% of total GHG emissions in 2010 [2]. The International Energy Agency (IEA) set up a BLUE Map scenario with 14 Gt of CO2 emissions in 2050 compared with 57 Gt of CO2 emissions in the baseline scenario [3]. In order to achieve this target, carbon capture and storage (CCS) technology will play a vital role in delivering 19% of cumulative CO2  emission reductions between 2015 and 2050 in the power sector [3].

Among the three main approaches envisaged for CO2  capture from power plants—pre-combustion capture, post-combustion capture, and oxyfuel capture [4]—the solvent-based post-combustion carbon capture (PCC) process is regarded as the most promising technology for commercial deployment [5,6]. In solvent-based carbon capture  technology,  CO2 is separated  from  flue  gas after combustion by chemical absorption; monoethanolamine (MEA) is regarded as a benchmark solvent for this process.

《1.2.Previous studies》

1.2.Previous studies

A complex electrolyte aqueous solvent is involved in the MEA-based PCC process [7], which requires accurate thermodynamic modeling and physical properties calculations for its modeling. Thermodynamic data, especially regarding CO2 solubility, have been reported for 30 wt% MEA aqueous solutions [8,9] and for a wider MEA solution concentration range [10,11]. For the parameterization and validation of physical properties calculation methods of an MEA-H2O-CO2 mixture, experimental data on MEA aqueous solutions are valuable, especially with different CO2 loading. Correlations for the calculation of the density and viscosity of MEA-H2O-CO2 mixtures at different temperatures and MEA concentrations can be found in the literature [12–14]. In terms of mass transfer and thermal performance of the integrated MEA-based PCC process, several experimental campaigns [15,16] have been conducted.

For a highly nonlinear electrolyte MEA-H2O-CO2 solution, the electrolyte non-random  two-liquid  (eNRTL)  model  [17,18]  is  the most widely adopted model [10,19]. Recently, some studies [20,21] have also used the perturbed-chain statistical association fluid theory (PC-SAFT) [22,23] equation of state (EOS) for the vapor phase of an MEA-H2O-CO2  mixture, with a system temperature of up to 500 K and a system pressure of up to 15 MPa.

For this capture process, significant energy is consumed for solvent regeneration [6]. Thus, the cost of carbon capture is high when PCC is added to the emitters. Great research efforts have been taken to reduce the carbon capture cost through process modeling and simulation approaches. Most early studies were carried out for the parametric sensitivity analysis of solvent-based PCC processes in the context of coal-fired power plants [24–27]. Some studies were carried out on integrations between power plants and carbon capture plants [28–30]. Several studies focused on optimizing the  whole plant through process optimization [31–34].

However, obvious inconsistencies in the literature were found for key equipment design features and key operational variables. For example, the packing height varies from 13.6 m [35] to 30.6 m [32] for the absorber and from 7.6 m [35] to 28.15 m [21] for the stripper for similar capture tasks. The optimal lean loading range is equally wide from 0.132 molCO2·  [31] to 0.234 molCO2·  [36], with corresponding specific duty in a range from 3.77  to 4.35 .Those inconsistencies cause confusion for future research in this field. They may also cause some trouble for the engineering design of a large-scale commercial deployment.

The main reasons for the abovementioned knowledge gaps may be related to conflicts between the complexity of the integrated system and the accuracy requirement of the modeling and simulation studies. Firstly, the models used in some publications were relatively simple. For example, equilibrium models were used for the mass transfer and reaction in both the absorber and the stripper [37]. For a rate-based model, the correlations for calculations of mass transfer coefficients, interfacial area, liquid holdup, and pressure drop inside packing beds also have a large impact on the prediction accuracy [38,39]. For the kinetics-controlled reactions, it is found that the values of the kinetics of reverse reactions for bicarbonate formation are different for the absorber and the stripper [40]. Inappropriate correlations used in the models would significantly affect the accuracy of model predictions.

《1.3. Aim and novel contribution》

1.3. Aim and novel contribution

In order to address the abovementioned knowledge gaps, this study aims to improve the accuracy of the rate-based model in Aspen Plus® for the MEA-based carbon capture process. The novel contributions of this paper can be justified by the following: ① A new combination of correlations was selected after comparing model predictions with the experimental vapor-liquid phase equilibrium (VLE) data; ② the correlations for predicting the liquid density of the  mixture  and  the  effective  vapor-liquid  interfacial  area  were improved by coding Fort an subroutines in Aspen Plus®; ③ different kinetics parameters were used for reverse reactions for bicarbonate formation in the absorber and the stripper, respectively, thus reflecting the nature of the different operating conditions in the absorber and the stripper; and ④ the rate-based process model was validated with the experimental data and pilot plant data at three different stages, including thermodynamic modeling, physical properties calculations, and process model development at the pilot scale.

《2.Framework of modeling of the solvent-based carbon capture process》

2.Framework of modeling of the solvent-based carbon capture process

Using an amine solvent to absorb CO2 from exhaust gases is a reactive absorption process involving an electrolyte aqueous solvent [6]. The modeling of this non-ideal multi-component system is a systematic work at different levels. Fig. 1 outlines the modeling framework for such a PCC process. Although the software package Aspen Plus® was used for the modeling and simulation of the process, it is important to check the calculation methods with their corrections in order to ensure the accuracy of the process simulation and  optimization.

《Fig. 1》

Fig. 1. Framework of the modeling of a solvent-based PCC process. L: liquid phase; V: vapor phase.

Accurate prediction of the physical properties of pure components and mixtures is one of the basic prerequisites in process modeling and simulation. As the first step, the thermodynamic model should be developed to present VLE and to calculate the state parameters of the MEA-H2O-CO2 mixture, such as the temperature, pressure, and composition of the liquid and vapor phases. The solubility of CO2  in the MEA-H2O-CO2   mixture is a key parameter, and is normally used for validation purposes for the calibration of the correlations or for selection for VLE calculation.

The physical properties are part of the correlations for heat transfer, mass transfer, interfacial area, liquid holdup, and pressure drop. It is important to choose the right physical property models to ensure the success of process modeling and simulation.

At  the  process  level,  both  absorption  and  desorption  in  the packed columns are key processes. A rate-based model offers better accuracy than an equilibrium model for the absorption performance of the columns [41]. This accuracy is a function of the appropriate correlations used for liquid and vapor phase mass transfer coefficients, the effective vapor-liquid interfacial area, and the pressure drop in the rate-based model.

This framework shows that the rate-based model for this solvent based carbon capture process is a highly nonlinear model, which has numerous parameters, correlations, and equations. Therefore, it is not realistic to completely repeat the published models with the same input conditions. This is also the main consideration behind the choice to use three-stage validations in this study, and to update some correlations by coding a Fortran subroutine in Aspen Plus® to ensure model accuracy, rather than directly comparing the process performance with those of other published models. Using this three stage model validation method, the model was dissected in detail based on the logical structure of numerical modeling, allowing more insights to be obtained.

《3.Thermodynamic modeling of the MEA-H2O-CO2 system》

3.Thermodynamic modeling of the MEA-H2O-CO2 system

《3.1.EOSs and relevant model parameters》

3.1.EOSs and relevant model parameters

In this study, the PC-SAFT EOS [22,23] is used to calculate the properties of the vapor phase, and the eNRTL [18] method is used to model the electrolyte system of an MEA-H2O-CO2 mixture.

3.1.1. The PC-SAFT EOS for the vapor phase

Compared with some typical cubic EOSs such as the Peng-Robinson (PR) EOS and the Soave-Redlich-Kwong (SRK) EOS, the PC-SAFT EOS is able to accurately estimate vapor phase fugacity coefficients at high pressures [20,42], which is important for accurate performance predictions of CO2 compression with an outlet pressure as high as 136 bar [43]. Table 1 [20,23,44] summarizes the PC-SAFT parameters of pure components, and Table 2 [45,46] lists the binary interaction parameters (kij) of MEA-H2O and CO2-H2O.

1 bar = 105 Pa.

《Table 1》

Table 1 PC-SAFT parameters of pure components.

《Table 2》

Table 2 Binary parameters for the PC-SAFT EOS.

3.1.2. The eNRTL method for the liquid phase

The liquid phase of an MEA-H2O-CO2 mixture is a typical electrolyte solution [19]. The eNRTL method has been validated and used to model electrolyte solutions in many publications [19,20,27,40,47].

Table 3 [20,44,46] summarizes the model parameters used for this study, and their sources. Most of the parameters were obtained from the SRK-ASPEN databank [44], and some were updated by recent studies by regression using new experimental data [46].

《Table 3》

 Table 3 Model parameters for eNRTL.

《3.2. Physical solubility and Henry’s law constant》

3.2. Physical solubility and Henry’s law constant

Physical solubility is the equilibrium between CO2   molecules in the vapor phase and those in liquid solutions; it is calculated using Henry’s law. The Henry’s law constants for CO2  with water and with MEA are required, and can be calculated using Eq. (1):

where Hi−j  is the binary Henry’s law constant between pure components i and j; T is the system temperature; and C1, C2, C3, and C4 are correlations for Henry’s law constants. Table 4 [46,47] summarizes the available binary Henry’s law constants for an MEA-H2O-CO2 mixture. For the system of an MEA-H2O-CO2  mixture, most publications only take gas components such as CO and N2  as Henry components In addition, most studies only consider Henry’s law constants for CO2 with H2O [19]. The Henry’s law constants for CO2 with H2O have been well studied by Yan and Chen [46] by examining extensive quantities of experimental VLE data for the CO2-H2O binary system. Liu et al. [47] considered Henry’s law constants for CO2  with MEA.

《Table 4》

Table 4 Correlations for the calculation of Henry’s law constants (on the molality scale).

《3.3. Chemical reaction equilibrium》

3.3. Chemical reaction equilibrium

Liquid phase chemical reactions involved in the MEA-H2O-CO2 system can be expressed as follows:

R1: water dissociation

R2: dissociation of CO2

R3: dissociation of carbonate

R4: dissociation of the protonated amine

R5: carbonate formation

The chemical equilibrium constants of these reactions were calculated using Eq. (2), and the related correlations are shown in Table 5 [19,48,49].

《Table 5》

Table 5 Correlations for chemical equilibrium constants (on the molality scale).

where Kj refers to the chemical equilibrium constants for each reaction j; T is the system temperature; and C1, C2, C3, and C4 are correlations for the chemical equilibrium constants.

Once  the  chemical  equilibrium  constants  are  determined,  the chemical equilibrium of each reaction is determined using Eq. (3) [19].

where i denotes the  reactant  component; n denotes the  product component; x denotes the model fraction of each component in the liquid phase based on true species, molecular and ionic; γ denotes the activity coefficient; and ν denotes the stoichiometric coefficient of each component in reaction j.

《3.4.Validation of CO2 solubility prediction》

3.4.Validation of CO2 solubility prediction

3.4.1.Case setup

In order to compare and select appropriate correlations for this study, several combinations of correlations [19,20,47] were chosen for carrying out the validation against the experimental data. Table 6 [19,20,46,47,50,51] provides the model details.

《Table 6》

Table 6 Different combinations of correlations for validation.

《Table 7》

Table 7 MAPE of validation with CO2 partial pressure of the MEA-H2O-CO2 system.

3.4.2.Validation results

For model validation purposes, the model predictions were compared with the experimental data in terms of the CO2 partial pressure and/or total pressure in the vapor phase for different CO2 loading in an MEA aqueous solution. In this study, the experimental data from Ref. [11] were chosen because these data cover a wider range of  MEA concentrations than  other publications,  as well as  wider ranges of system temperatures and pressures.

Fig. 2 depicts comparisons between model predictions and experimental data for the partial pressure of CO2 in the vapor phase of MEA-H2O-CO2 mixtures for different concentrations of MEA. Table 7 [19,20,47] presents the mean absolute percentage error (MAPE) of validation results at different MEA concentrations. Generally, the deviations between experimental data and model predictions become bigger at the lower (15 wt%) and higher (45 wt%–60 wt%) MEA concentrations, compared  with  the  30  wt%  MEA  concentration. It is noticeable that the model predictions of this study at 15 wt% concentration are worse than those of Ref. [47]. The reason is that some of the correlations used in this study were inherited from Ref. [20]. Furthermore, none of these four combinations produced good predictions that covered low to high MEA concentrations, which reflects an inherent limitation of the correlation method: A correlation should not go beyond the conditions of the data for its regression. However, most of existing correlations that were used for the thermodynamic modeling of the MEA-H2O-CO2 system were regressed based on experimental data at 30 wt% MEA concentration solvent.

《Fig. 2》

Fig. 2. CO2  partial pressure as a function of CO2  loading with: (a) 15 wt% MEA solvent, (b) 30 wt% MEA solvent, (c) 45 wt% MEA solvent, and (d) 60 wt% MEA solvent. Exp: experimental data; TS: this study.

《4. Physical properties of the MEA-H2O-CO2 system》

4. Physical properties of the MEA-H2O-CO2 system

《4.1.Physical property model》

4.1.Physical property model

The physical properties include: ① thermodynamic properties, such as density, enthalpy, and heat capacity; and ② transport properties, such as viscosity, surface tension, thermal conductivity, and diffusivity. Table 8 lists the chosen models for the property calculation for the mixture in this study. It should be noted that the correlations for the density of the liquid mixture are obtained from Ref. [14] by coding a Fortran subroutine in Aspen Plus®.

《Table 8》

Table 8 Correlations used for the property calculation of the mixture.

《4.2.Available experimental data for validation》

4.2.Available experimental data for validation

Table 9 [14,52–54] provides the available experimental data from the literature for the physical properties validation of MEA-H2O-CO2. The vapor phase of the MEA-H2O-CO2 mixture under the operating temperature (20–150 °C) and pressure (1–2 bar) of the absorber and stripper is not an issue, and no experimental data are available for those properties of  the vapor phase. Available experimental data for the thermal conductivity of the liquid phase were not currently found. Furthermore, direct measurement of CO2 diffusivity in an MEA aqueous solution is impossible because CO2 reacts with MEA. The NO2 analogy method was used to produce the data for CO2  diffusivity [55].

《Table 9》

Table 9 Available experimental data for the physical properties of the liquid phase.

《4.3.Validation results》

4.3.Validation results

Fig. 3 and Fig. 4 present comparisons between the model predictions and the experimental data for different properties of the MEAH2O-CO2 mixture at different concentrations of MEA.

Table 10 presents the deviations of the validation results for the physical properties plotted in Fig. 3 and Fig. 4. Both MAPE and the maximum absolute percentage error (APE) are given. For the liquid density, the model predictions are in good agreement with the experimental data over the full range of system conditions. For the specific heat capacity (Fig. 4(a)), the deviations gradually increase as CO2 loading rises. For the surface tension, the experimental data themselves have large deviations (Fig. 4(c)).

《Fig. 3》

Fig. 3. Liquid density of MEA-H2O-CO2 with different CO2  loading ( ) at: (a) 30 wt% MEA solvent, (b) 40 wt% MEA solvent, (c) 50 wt% MEA solvent, and (d) 60 wt% MEA solvent. Model: model predictions.

《Fig. 4》

Fig. 4. Physical properties of MEA-H2O-CO2 at different MEA concentrations and 298.15 K for: (a) specific heat capacity, (b) liquid viscosity, and (c) surface tension.

《Table 10》

Table 10 Deviations of the model predictions from the experimental data.


《5. Process model development and validation at the pilot scale》

5. Process model development and validation at the pilot scale

《5.1. Introduction of the pilot plant》

5.1. Introduction of the pilot plant

In this study, the pilot plant located at the University of Kaiserslautern [56] was chosen for model validation, for the following reasons: ① Both the absorber and the stripper use Mellapak 250Y packing, which is regarded as appropriate structured packing for industrial deployment [57]; and ② the experimental data are comprehensive and well presented in the publications from this plant [16], which enables more comprehensive validation that can be compared with other studies. Table 11 summarizes the equipment features and the ranges of the key operation variables. (For more details about this pilot plant, refer to Ref. [16].)

《Table 11》

Table 11 Main specifications of the pilot plant.

《5.2. Process model development》

5.2. Process model development

5.2.1Model flowsheet and process description

Fig. 5 shows the flowsheet of this steady-state process model in Aspen Plus®. The flue gas leaving the power plant goes to a gas blower and is then cooled to 40–50 °C before entering the absorber, in order to improve the absorption efficiency [25]. The scrubbed flue gas is emitted to the atmosphere and the CO2-rich solvent is discharged from the bottom of the absorber and enters the stripper. The CO2-rich solvent is regenerated inside the stripper with heat input to the reboiler. The regenerated solvent is cooled and re-circulated to the absorber for reuse.

《Fig. 5》

Fig. 5. Process flowsheet in Aspen Plus®. MU: make up; REB: reboiler; CONDSPL: condensate splitter; DESUPSTM: desuperheater steam; INTHEX: internal heat exchanger; DCC: direct contact cooler; WAT: water desuperheater.

5.2.2.Kinetics-controlled reactions

In Section 3.3, the equilibrium reactions of the MEA-H2O-CO2  mixture  were  described  during  the  thermodynamic  modeling.  In the rate-based model, the reaction of dissociation of CO2 and the reaction of carbonate formation should be considered as kinetics-controlled reactions [27]:

R2*: dissociation of CO2

R5*: carbonate formation

Power law expressions were used for the kinetics-controlled reactions. The reaction rates of reactions R2* and R5* can be calculated by Eq. (4) [27].


where rj  is the reaction rate for reaction j in mol·(min·m3 )-1; koj is the pre-exponential factor in kmol·(m3·s)–1; T is the system temperature in K; n is the temperature factor; Ej  is the activation energy in kJ·mol-1 ; R is the gas constant; Ci is the mole fraction of species i; and αij is the reaction order of component i in reaction j. koj and Ej for the reactions were calculated using the experimental data shown in Table 12 [40].

《Table 12》

Table 12 Parameters kjo and Ej in Eq. (4) [40].

5.2.3. Rate-based mass transfer

The modeling of the absorber and the stripper was based on two film theory [58], which is used to describe the mass transfer of components between the gas phase and the liquid phase. According to two-film theory, a vapor film and liquid film with a phase equilibrium interface are assumed between the bulk gas and bulk liquid phases. Chemical reactions are assumed to occur in the liquid film only.

For the Rate Sep model in Aspen Plus®, Zhang et al. [27] provided very detailed discussions about correlations and settings. In this study, the VP lug flow model was chosen in order to model the bulk properties with reasonable accuracy, since the “Countercurrent” model sometimes causes oscillations in the temperature  profile even though it is  the closest approximation of the  real situation [59]. It was also noted that the discretization points of the liquid film need to be over 10, in order to achieve accuracy; otherwise, the simulation results may exhibit over-predictions of the rate of mass transfer.

For the correlations related to mass transfer, Razi et al. [59] validated 12 correlation combinations with experimental data from the CO2 enhanced separation and recovery (CESAR) pilot data; the results show that Billet and Schultes [60] give an accurate correlation. Table 13 [59–62] lists the parameters and correlations related to mass transfer that were used in this study. Here, a Fortran subroutine was used to implement the correlation of Ref. [61] for the calculation of interfacial area.

《Table 13》

Table 13 Parameters and correlations selection for mass transfer in the RateSep model.

《5.3.Model validation》

5.3.Model validation

For the MEA-based carbon capture process, the key operational parameters affecting performance are the CO2 concentration in the flue gas, the MEA concentration in the solvents, the lean loading, and the liquid-to-gas (L/G) ratio. Thus, four sets of experiments from Ref. [16] were chosen for model validation purposes. These include: ① Experiments A1–A6, with different CO2   concentrations in the flue gases; ② Experiments A24–A27, with different MEA concentrations  at  two  different  CO2   concentrations  in  the  flue  gases; ③ Experiments A28–A33, with different solvent flow rates at high CO2 concentrations in the flue gases; and ④ Experiments A34–A39, with different solvent flow rates at low CO2 concentrations in the flue gases. Model validations were carried out based on the same feed conditions, and the CO2 loading in the lean solvent (lean loading) was targeted by varying the reboiler duty of the stripper. Experimental data and model predictions for CO2 loading in a rich solvent (rich loading), the CO2 capture level, and the stripper reboiler duty could then be compared.

Fig. 6 illustrates the bias between experimental data and model predictions for CO2 capture level, rich loading, and specific duty under the same input conditions. The validation results for CO2 capture level and rich loading show a good agreement. Regarding the specific duty, reboiler duty in the experiments was affected by heat loss from the equipment and pipelines, which could not be measured directly. Although the values for specific duty provided in Ref. [16] were corrected, the deviations themselves could not be evaluated, which may be the reason for high APEs for the validation results of the specific duty. MAPEs of  the model predictions for the CO2 capture level, the stripper reboiler duty, and the rich CO2 loading, as compared with the experimental data from Ref. [16], are 1.78%, 1.54%, and 7.49%, respectively.

《Fig. 6》

Fig. 6. The bias between experimental data and model prediction for: (a) CO2 capture level, (b) rich loading, and (c) specific duty.

Validations were also conducted to compare the temperature profiles and the CO2 composition profiles inside the absorber and the stripper based on Experiments A1, A2, and A3 [16]. CO2 concentrations in the flue gases are 8.5 mol% for A1, 16.5 mol% for A2, and 5.5 mol% for A3. Fig. 7 shows that the model predictions are in very good agreement with the experimental data. One statement is that the total packing height is 2.25 m inside the stripper; the 3 m position of the packing in Fig. 7(b) and Fig. 7(d) represents the reboiler of the stripper. The comparison results show that the model predictions are in very good agreement with the experimental data.

《Fig. 7》

Fig. 7. Validation results between model predictions and experimental data. (a) Temperature profile of the absorber; (b) temperature profile of the stripper; (c) CO2 composition profile inside the absorber; (d) CO2 composition profile inside the stripper.

《6.Case study》

6.Case study

《6.1.Methodology for model scale-up from pilot scale to commercial scale》

6.1.Methodology for model scale-up from pilot scale to commercial scale

To match the capacity requirement of handling the flue gas from power plants at an industry scale, the model of the carbon capture process at a pilot scale was scaled up, based on chemical engineering principles regarding the estimation of column diameter and pressure drop [63].

As initial inputs to the process model at an industrial scale in Aspen Plus®, first-guess diameters are required for both the absorber and the stripper. The column diameters can be calculated from the maximum flooding vapor. In this study, a generalized pressure drop correlation (GPDC) figure (Fig. 8) [64] was used to estimate the maximum flooding vapor. The abscissa and ordinate are presented in Eq. (5) and Eq. (6) [64], respectively.

《Fig. 8》

Fig. 8. Generalized pressure drop correlation [64].

In Eq. (5), FLV  is a flow parameter. For the absorber, the liquid feed is the lean solvent. Its flow rate can be estimated using Eq. (7) [21].

where FLean is the mass flow rate of the lean solution; FFlue is the mass flow rate of the flue gas; xCO2  is the mass fraction of CO2 in the flue gas; ψCO2  is the required carbon capture level; MMEA is the molar weight of MEA; α Rich and αLean  are the CO2   loading () in rich solvent and lean solvent, respectively; and ωMEA is the MEA mass fraction in solvent.

From Eq. (6) , V*W (vapor mass flow rate per unit cross-sectional area) is calculated; next, the total cross-sectional area can be obtained given the flue gas flow rate. In this equation, K4 is a load parameter obtained from Fig. 8, according to the value of FLV and the specified pressure drop; FP  is a packing factor.

In order to achieve good liquid and gas distribution and to avoid  flooding inside packing beds, a pressure drop of  15–50 mmH2O per meter of packing for absorber and stripper was recommended [64]. In this study, a maximum pressure drop per unit height of 20.83 mmH2O [65] was used, considering the formation of the MEA solvent [21]. It should be noted that the design of the column internals, such as gas/liquid distributors and re-distributors, is crucial in order to ensure good gas and liquid distribution inside the absorber and regenerator for such large diameters.

1 mmH2O = 9.8066136 Pa.

《6.2. Carbon capture from a 250 MWe CCGT power plant》

6.2. Carbon capture from a 250 MWe CCGT power plant

In this case study, the carbon capture plant was scaled up to handle the flue gas from the 250 MWcombined cycle gas turbine (CCGT) power plant described in Ref. [66]. For comparison purposes, the input conditions of the flue gas and the operating conditions of the columns were chosen to be same as for the case without exhaust gas recirculation (EGR) in Ref. [66]; these conditions are presented in Table 14.

《Table 14》

Table 14 Boundary conditions of the solvent-based PCC process.

It is noticeable that the upper limit of  the pressure drop per height of packing bed is 42 mmH2O in Ref. [66]; at this value, the  columns may  have  serious  flooding  [21].  In  this  case  study,  the flooding factors of the columns were set up to 65%. Another consideration is that structured packing is preferred for a large-diameter absorption column, due to the possibility of serious mal-distribution of both the liquid and vapor phases inside the random packing bed [67]. Therefore, Mellapak 250Y packing, which is regarded as an appropriately structured packing type for industrial deployment [57], was chosen for the packing beds inside the absorber and the stripper in this case study.

The first-guess diameters of the absorber and the stripper can be calculated using the method presented in Section 6.1. Starting from that point, these parameters were simulated using the improved rate-based model developed in Aspen Plus®. Table 15 summarizes a comparison of the results of this study with those presented by Canepa et al. [66], in terms of equipment size and process parameters. It shows that the design of this study achieved smaller equipment size and lower thermal duty.

《Table 15》

Table 15 Comparison results between this study and the literature.

a Two columns with same diameter of 9.5 m.

One significant difference is that the packing heights in both the absorber and the stripper in this study are significantly smaller than those in Ref. [66]. One contributor is that higher efficiency structured packing was used in this study, which allows a lower height of transfer unit (HTU); the total packing height was then reduced, while keeping the same number of transfer units (NTU). At the same time, using the more accurate model provides more confidence in the simulation results, such that a conservative margin may not be needed. After all, capture levels reached as high as 90%–95% during experiments with short packing beds for both the absorber and the stripper of the pilot plants. In the experimental study by Dugas [15], the packing heights of both the absorber (with IMTP No. 40 random packing) and the stripper (with Flexipac 1Y) are 6.1 m. Notz et al. [16] reported the packing heights of the absorber and the stripper (with Mallepak 250Y) to be 4.2 m and 2.25 m, respectively, for their pilot plant.

It is also noticeable that the specific duty in this study is lower than that in Ref. [66]. The results in Table 15 show that the rich loading of this study is slightly higher, which may reflect the impact of thermodynamic modeling. With the same CO2 concentration in the flue gas, the solubility of CO2 that is calculated by this improved thermodynamic model may be slightly higher when close to its saturation. This situation also results in a smaller flow rate of the solvent entering the stripper. Thus, the heat requirement for solvent evaporation in the stripper decreases. At the same time, using more accurate kinetics for the reactions inside both the absorber and the stripper could also have a large impact on the predictions of heat requirement for solvent regeneration, although it is hard to dissect those highly nonlinear relations between those factors.



This paper presented a study on the development of an accurate rate-based steady-state model in Aspen Plus®, with some elements implemented in Fortran subroutines, for the MEA-based carbon capture process, along with a case study using this model. It was found that  the  correlations  of the  thermodynamic  model have  a significant impact on the prediction accuracy of the VLE of the MEAH2O-CO2 mixture. A new combination of correlations was selected in this study, and shows better prediction performance. Following this step, the model from Ref. [14] was used, by coding a Fortran subroutine in order to improve the prediction accuracy of the liquid mixture density. The rate-based process model was improved by setting different kinetics for the reverse carbonate formation reactions in the absorber and the stripper, respectively, and by coding a Fortran subroutine for the effective gas-liquid interfacial area using the model from Ref. [61]. The model validation results show that the model predictions appear to be in good agreement with the experimental data from the pilot plant.

Using this accurate model, a case study was carried out for carbon capture fitted to a 250 MWe CCGT power plant. The results show that this study achieved smaller equipment size and lower energy consumption than the previous study; these results may translate into significant savings in both capital investment and utility cost for the carbon capture plant.



This work was supported by the EU FP7 Marie Curie International Research Staff Exchange Scheme (PIRSES-GA-2013-612230).

《Compliance with ethics guidelines》

Compliance with ethics guidelines

Xiaobo Luo and Meihong Wang declare that they have no conflict of interest or financial conflicts to disclose.



C            Correlations for property calculations

E           Activation energy

Fp           Packing factor

FLV          Flow parameter

H            Henry’s law constant

ko         The pre-exponential factor

K           Chemical equilibrium constants of reaction j

L             Total liquid flow rate in mass

P            Pressure

R            Ideal gas constant

rj            Reaction rate of reaction j

T            Temperature

V            Total vapor flow rate in mass

V*       Vapor mass flow rate per unit cross-sectional area

x             Liquid-phase model fraction based on true species, molecular and ionic

y             Vapor-phase mole fraction

《Greek Letters》

Greek Letters

α             CO2  loading in lean solvent or rich solvent

αij           Reaction order of component i in reaction j

ρ             Density

ψCO2       CO2capture level

ωMEA       MEA mass fraction in solvent

g              Activity coefficient

v              Stoichiometric coefficient of each component in reactions



o             Standard state



CO2         CO2  component

Flue         Flue gas

Lean        Lean solvent

L              Liquid phase

MEA        Monoethanolamine

V             Vapor phase

 i             Reactant  component

j              Chemical reaction

n             Product component