Influence of five model parameters on the performance of a CO2 absorber column by a loaded aqueous MEA solution
Fossil Energy Valorization Laboratory, Chemical Engineering Department, National Polytechnic School, 10 Avenue HassenBadi, BP 182, El Harrach, Algiers, Algeria
2 CY Cergy, Paris Université, 33 Boulevard du Port, 95000 Cergy, France
* Corresponding author: email@example.com
Accepted: 10 December 2020
Rigorous packed-bed absorber modeling and simulation are significant for post-combustion CO2 capture processes design. Hence, a good knowledge and judicious selection of model parameters are essential to ensure reliable predictions. In this paper, the reactive absorption of CO2 into loaded aqueous monoethanolamine solution was modeled, furthermore, the effects of five different parameters (kinetic model, enhancement factor, enthalpy of absorption, CO2 diffusivity, and vapor pressure) were investigated. Finally, this study revealed that some model parameters have a large influence on the column performance, contrary to others. In addition, methods and correlations that generally provide more accurate predictions of the empirical data relative to the other cases involved in this research were determined for each model parameter. It was also found that the model deviation was reduced by 18% and 4% for the liquid temperature and liquids CO2 loading profiles, respectively, while comparing between the worst and the best case.
© I. Hammouche et al., published by IFP Energies nouvelles, 2021
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Significant efforts have been made to reduce greenhouse gas emissions and mitigate the global warming IPCC (2007). In this area, particular attention has been given to carbon dioxide removal using the post-combustion process on the basis of the absorption-desorption with chemical solvents.
In recent years, remarkable progress has been made in this area of research. New solvents that increase the CO2 absorption and desorption of CO2 have been developed such as the nanoparticle additives and biphasic solvents (Liu et al., 2019; Mehassouel et al., 2018; Wang et al., 2016). Despite that, aqueous solution of monoethanolamine is undoubtedly still the most extensive, mature, appropriate, and well-documented chemical solvent for the CO2 capture in post-combustion processes (Akinola et al., 2019; Ali Saleh Bairq et al., 2019; Gheni et al., 2018; Mohammadpour et al., 2019; Wang et al., 2019).
Reliable dimensioning, scaling, and monitoring post-combustion processes require the use of an accurate packed bed absorber modeling and simulation (Llano-Restrepo and Araujo-Lopez, 2015). Therefore, a good knowledge and judicious selection of model parameters are essential to ensure rigorous predictions. In this context, several studies on diverse process parameters have been published in literature (Abu-Zahra et al., 2007; Afkhamipour and Mofarahi, 2013, 2014; Khan et al., 2011; Kvamsdal and Hillestad, 2012; Kvamsdal and Rochelle, 2008; Mofarahi et al., 2008; Wu et al., 2010).
Abu-Zahra et al. (2007) investigated absorber inlet temperature, lean amine loading, concentration, and stripper pressure sensitivities. Furthermore, the process that recovers carbon dioxide from flue gas was studied by Mofarahi et al. (2008) where the effect of both operating conditions and design parameters on the absorber and stripper columns was presented. In addition, a statistical analysis and a combination between the sensitivity analysis and the neural networks modeling were carried out by Wu et al. (2010) to investigate the interactions between similar main process parameters. However, the studies on the selection of rate-based model parameter correlations in packed columns are scarce. Khan et al. (2011) applied the rate-based model to perform a sensitivity analysis using different mass transfer coefficients in a packed column. Furthermore, in the aim of determining the temperature bulge position and magnitude, Kvamsdal and Rochelle (2008) carried out a comparative analysis between diverse methods that compute mass transfer coefficient, liquid heat capacity, and liquid density. Moreover, Kvamsdal and Hillestad (2012) focused on the selection of the rate-based model parameter correlations for prediction of physical properties and kinetics, they investigated also their effects on mass and energy balances. Afkhamipour and Mofarahi (2013, 2014) performed a rate-based model sensitivity analysis, once by varying different mass transfer correlations, and another time by combining these correlations with different kinetic models.
In the current paper, the reactive absorption of CO2 with loaded aqueous monoethanolamine solution in a packed-bed absorber was modeled and simulated, in addition, the effects of five different parameters (kinetic model, enhancement factor, enthalpy of absorption, CO2 diffusivity in aqueous solution of MEA, and vapor pressure) on the column performance were investigated by performing a parametric study based on step-by-step approach.
For modeling the CO2 absorption in a packed column, three forms of the rate-based model were developed: the Continuous Differential Contactor (CDC) model, the Continuous Film Reaction (CFR) model, and Non-Equilibrium Stage (NEqS) model. In this study, the last version of the CDC model was applied, indicating that this model was first proposed by Pandya (1983) based on the differential-change approach suggested by Treybal (1969) which was largely employed by many authors, and since it presents some inconsistent simplifications or assumptions, Llano-Restrepo and Araujo-Lopez (2015) revised the model using the finite difference approach and introduced a new improved version as shown below:
CO2 and water vapor balances for the gas phase:
The mole fractions of CO2 and water vapor yA.I and yS.I, respectively, at the interface are given below:
Noting that, the model considers the liquid phase as an ideal solution. Thus γS = 1 and Raoult’s law is valid.
A total mole balance for both liquid and gas phases:
Temperature gradients for the gas and liquid phases:
Indicating that the energy balance neglects heat losses through the wall of the absorber column (an adiabatic column is assumed).
For the resolution of these differential equations a computer program was coded in Matlab software.
The correlations used for estimating the different physicochemical and transport properties are listed in Table A1.
A large number of experimental and theoretical studies have been recorded in literature on the kinetics of the reaction between CO2 and an uncharged aqueous MEA since 1950s (Blauwhoff et al., 1983; Danckwerts and Sharma, 1966; Faramarzi, 2010; Freguia and Rochelle, 2003; Hikita et al., 1977, Horng and Li, 2002; Jamal et al., 2006; Kucka et al., 2002, 2003; Kvamsdal et al., 2009; Luo et al., 2012; Pinsent et al., 1956; Plaza, 2011; Versteeg et al., 1996; 1979; Ying and Eimer, 2013). However, only few researchers have studied kinetics of CO2 absorption into partially carbonated MEA solutions (Aboudheir et al., 2003; Dang and Rochelle, 2003; Dugas and Rochelle, 2011; Littel et al., 1992; Luo et al., 2015; Puxty et al., 2010). Among these research works, only two termolecular kinetic models of carbon dioxide reacting with loaded aqueous MEA solution have been found (Aboudheir et al., 2003; Luo et al., 2015), Table A2 includes both models, the mechanism that they are based on and their validity ranges.
Since the last century, many researchers have studied the enhancement factor used to compute the mass transfer rates from gases to liquids (Brian et al., 1961; Cussler, 2009; DeCoursey, 1982; DeCoursey and Thring, 1989; Gaspar and Fosbøl, 2015; Gilliland et al., 1958; Hatta, 1928; Hikita et al., 1982; Hogendoorn et al., 1997; Last and Stichlmair, 2002; Van Krevelen and Hoftijzer, 1948; Van Swaaij and Versteeg, 1992; Van Wijngaarden et al., 1986; Versteeg et al., 1989; Wellek et al., 1978; Yeramian et al., 1970). Consequently, a great variety of models have been developed (Brian et al., 1961; Cussler, 2009; Gaspar and Fosbøl, 2015; Last and Stichlmair, 2002; Van Krevelen and Hoftijzer, 1948; Wellek et al., 1978; Yeramian et al., 1970). A list of expressions allowing the calculation of enhancement factor is provided in Table A3.
Although MEA is believed to be the most commonly used solvent for the CO2 removal, only limited data on the direct measurements of the heat of absorption of the reaction between CO2 and aqueous solutions of MEA have been published (Arcis et al., 2011; Carson et al., 2000; Kim, 2009; Kim and Svendsen, 2007; Mathonat et al., 1998).
Few researchers, in their works, used fixed values for the enthalpy of absorption prediction (Kohl and Nielsen, 1997; Kvamsdal and Rochelle, 2008; Pandya, 1983), however, others have developed new correlations to calculate the enthalpy of absorption on the basis of existing empirical data available in literature (Kim, 2009, Llano-Restrepo and Araujo-Lopez, 2015). All these correlations and fixed values for the heat of absorption estimation are summarized in Table A4.
Over the years, various temperature dependent equations for water vapor pressure estimation have been developed. In this study, we will consider the ones that are most commonly used in literature (Ambrose and Walton, 1989, Antoine, 1888, Riedel, 1954). These correlations are written in Table A5.
Because of the reaction occurring between CO2 and the amine solutions, the CO2 diffusivity in the MEA solution cannot be determined directly. Therefore, Clarke (1964) proposed the N2O analogy method which has been then adopted by many researchers. This approach can be expressed as follows:
Various measurements have been reported in literature on the N2O diffusivity in aqueous solutions of MEA on a broad range of MEA concentration and temperature (Clarke, 1964; Cullen and Davidson, 1957; Ko et al., 2001; Li and Lai, 1995; Sada et al., 1978; Ying and Eimer, 2012). Based on these experimental data, different correlations have been developed which are listed in Table A6.
The effects of changing the empirical correlations or fixed values on the column performance (liquid temperature and liquid CO2 loading profiles) were investigated for each model parameter following step-by-step approach, that means, each time the best correlation for the parameter X calculation is determined then used (or fixed) to study the next parameter, and so on. For a better understanding, Table 1 shows the different methods used for model parameters calculation in each case.
Summary of the model parameter correlations used in each cases.
In this paper, the average relative deviation percentage ARD% was the criterion used to compare model predictions with experiments, and it was computed by using the following equation:
In this research work, the model simulation was based on the experimental data reported in the literature by Sonderby et al. (2013) using a pilot-scale CO2 absorption column, where 23 experiments were performed, denoted (R1–R23). However, in this study, only 9 runs (R 3, 8, 13–15, 18, 21–23) were taken into account, as shown in Table 2. This selection was founded on two criteria: a low experimental error and a large number of points (measurements) in each run.
All the simulation results, displayed in terms of liquid temperature and liquid CO2 loading average relative deviation percentages (ARD%s) for all runs (R 3, 8, 13–15, 18, 21–23) and all cases (21 cases), are summarized in Table 2.
In this section, the results presented in Table 2 are discussed in general terms, that means, they will be discussed only in respect of the simulation deviation between the different runs, and then a detailed discussion is given for each parameter separately (in Sects. 9.1–9.5).
The Case 5-c is selected as a base case for this discussion because it represents a combination between the correlations that provide the lowest ARD%. Accordingly, it has been noticed that for the liquid CO2 loadings, the ARDS% are in the range of 1–6%. A maximum ARD% of ±5% is obtained for runs (R13–15), while the minimum ARD% of ±2% is obtained for runs (R18, 21–23). Furthermore, for the liquid temperature, the ARDS% for the different runs are varying also between 1% and 6%. A maximum ARD% of ±5% is obtained for Run 18, an ARD% of 4.260% is also given by Run 8, while the other runs (R3, 13–15, 21–23) have an ARD% of ±2%. This difference in ARD%s between the different runs might be cause by the experimental errors which are different from a run to another, and it is believed that the reasons of these deviations are: The use of manual instruments instead of digital readouts which require a calibration prior to each run. Moreover, the difficulty to reach the adiabatic conditions at the laboratory scale which is assumed in the model development. Finally, pressure and heat losses along the column.
A sensible selection of kinetic models is important for obtaining accurate predictions. In regards to this area, the liquid-phase temperature and liquid CO2 loading have been simulated using two different kinetic models (Aboudheir et al., 2003, Luo et al., 2015).
Figure 1 illustrates the simulation results in respect of the empirical values obtained from runs R21 and R22 of Sonderby et al. (2013). According to this figure, it has been noticed that the liquid temperature and the liquid CO2 loading profiles simulated by using the kinetic model suggested by Luo et al. (2015) are more accurate than the ones obtained while using the kinetic model of Aboudheir et al. (2003). The use of the kinetic model of Aboudheir et al. (2003) is somewhat under predicts the liquid temperature and the liquid CO2 loading. This low accuracy presented by this kinetic model might be affected by the instrumental methods employed to obtain kinetic data, and the empirical correlations of physical properties (CO2 diffusivity and solubility in aqueous solutions) employed for kinetic model development. Furthermore, it has been also observed that the kinetic models have a large influence on both, liquid CO2 loading and liquid temperature. And according to the results obtained from Cases 1.a to 1.b for all runs, presented in Table 2, the same conclusions are found.
On the basis of the results presented in Table 2 for the Cases 2.a–2.h, it can be deduced that the effect of the different enhancement factor models on the performance of the columns is not very significant. The ARD%s between the simulation results and pilot-plant measured data are very close for all cases, the difference is in the order of ±0.1% for both liquid temperature and liquid CO2 loading. The lowest ARD% is obtained by using the model developed by Van Krevelen and Hoftijzer (1948), while the highest ARD% is given from the model suggested by Cussler (2009).
The simulated liquid temperature and liquid CO2 loading profiles were compared with measurements taken from runs R21 and R22 of Sonderby et al. (2013), as shown in Figure 2, to study the effect of the enthalpy of absorption on the absorber performance.
Simulation results obtained using different approaches of heat of absorption: (a) Kohl and Nielsen (1997), (b) Pandya (1983), (c) Kim (2009), (d) Llano-Restrepo and Araujo-Lopez (2015) based on Kim and Svendsen (2007) data and (e) Llano-Restrepo and Araujo-Lopez (2015) based on Arcis et al. (2011) data.
Accordingly, it has been noticed that the influence of the enthalpy of absorption on the liquid CO2 loading is very small, the different profiles obtained for R21 and R22 are almost overlapped, there is just a slight difference in the last 3 m of the absorber height, where the lowest ARD% is obtained from the correlation of Llano-Restrepo and Araujo-Lopez (2015) based on Arcis et al. (2011). On the other hand, the effect is very high for the liquid temperature profiles. It has been observed that the use of the fixed value of 118.2 kJ/mol, given by Kohl and Nielsen (1997), under-predicts the liquid temperature profile for both runs R21 and R22 with ARD%s of 5.573% and 5.746%, respectively. Furthermore, the liquid temperature profiles obtained by using the fixed value of 84.4 kJ/mol reported by Pandya (1983) and the two correlations developed by Kim (2009) and Llano-Restrepo and Araujo-Lopez (2015) based on Kim and Svendsen (2007) data are closely superposed with ARDs% of 7.793, 7.925, and 8.114%, respectively, for R21, and ARDs% of 5.881, 5.944 and 6.118%, respectively, for R22, they exhibit a good accord with experimental data in the initial 3 m then they over-predict the liquid temperature for the rest of the column. Finally, the correlation suggested by Llano-Restrepo and Araujo-Lopez (2015) based on Arcis et al. (2011) data provides the lowest ARD% of 2.522% and 2.521%, with respect to R21 and R22, the overall agreement in this case between simulated liquid temperature profile and measurements is generally good.
From the results of Table 2, regarding the Cases 3.a–3.e for all runs, it has been noticed that it leads to the same conclusions.
Different correlations for vapor pressure estimation (see Tab. A5) were employed to simulate the liquid temperature and the liquid CO2 loading using the experimental data of Sonderby et al. (2013), the results for R21 and R22 are illustrated in Figure 3.
As shown in Figure 3, the influence of the vapor pressure on both, the liquid temperature and the liquid CO2 loading is important. According to runs R21 and R22, the results obtained while using the correlation developed by Antoine (1888) show a good agreement with experimental data, contrary to the correlation suggested by Riedel (1954) where the ARD% is very large (see Tab. 2), it over-estimates the liquid temperature and the liquid CO2 loading, while the use of Ambrose and Walton (1989) correlation under-predicts them. All in all, and according to these results as well as the results shown in Table 2 for the Cases 4.a–4.d, it has been concluded that the correlation developed by Antoine (1888) leads to the most accurate simulation results.
The influence of CO2 diffusivity in aqueous solutions of MEA on the column performance is studied by using the measurements taken from Sonderby et al. (2013). The results, shown in Figure 4, are presented in terms of liquid temperature and liquid CO2 loading.
According to this figure, It has been noticed that the influence of the CO2 diffusivity in aqueous MEA solution on the column performance is not very large, all the three correlations show a good agreement with experimental data, the profiles obtained from the correlations of Ko et al. (2001) and Ying and Eimer (2012) are almost overlapped with very close ARD%s (see Tab. 2). However, the use of the correlation of Jamal (2002) under-predicts both, liquid temperature and liquid CO2 loadings profiles. The lowest ARD% is obtained from the correlation suggested by Ying and Eimer (2012), while the highest ARD% is given from the correlation developed by Jamal (2002), and according to Table 2 for the cases 5.a–5.c, the same is observed for the other runs.
In general, the experimental techniques, the number of measurements, as well as the assumptions behind the different model parameter correlations are the main reason of the discrepancies between experimental data and simulated profiles, therefore, a good selection of such correlations is of high importance which is the key objective of this parametric study. And in order to prove its significance on obtaining reliable modeling and simulation results, a comparison between a combination of different model parameters that present the highest and the lowest ARDs% was performed (only this two cases were chosen since the number of possible combinations is very large), in other words, the difference in deviation between the worst case and the best one is investigated in this section, Table 3 summarized the model parameter correlations used in each case.
List of the different model parameter correlations used in each case.
In general, the decrease of the model deviation between both cases is very large, it can reach more than 18% and 4% for the liquid temperature and liquid CO2 loading profiles, respectively, which is more important for the liquid temperature than for the liquid CO2 loading. This could be explained by the fact that almost all the model parameters studied in this paper have a great impact on the liquid temperature contrary to the liquid CO2 loading where the influence is minor. Finally, this comparison shows clearly the significance of this study on obtaining reliable model predictions seen the important reduction of the model deviation obtained while estimating the model parameters by the means of the most accurate correlations.
One of the challenges faced while modeling and simulating the reactive absorption of CO2 into loaded aqueous monoethanolamine solution in a packed-bed absorber is the proper calculation of the model parameters, hence, a parametric study was performed by using different cases of five different model parameters (kinetic model, enhancement factor, heat of absorption, CO2 diffusivity in aqueous solutions of MEA, and vapor pressure). Consequently, the following points can be deduced:
Among the model parameters studied in this paper, only kinetic model and vapor pressure have a large influence on the liquid CO2 loading.
Some parameters present a large influence on the liquid temperature (kinetic model, heat of absorption, and vapor pressure). Therefore, they should be chosen very carefully.
The effect of the enhancement factor and CO2 diffusivity is not very important, hence, a wrong choice of the model does not lead to severe deviation.
The kinetic model introduced by Luo et al. (2015), the widely used model developed by Van Krevelen and Hoftijzer (1948) for the enhancement factor prediction, the developed correlation of Llano-Restrepo and Araujo-Lopez (2015) on the basis of Arcis et al. (2011) data for the heat of absorption estimation, vapor pressure expression of Antoine (1888), and the correlation of Ying and Eimer (2012) for the diffusivity of CO2 in loaded aqueous MEA solution calculation generally provide more accurate predictions of the empirical values relative to the other cases employed in this analysis. This combination of correlations is obtained using the step-by step approach where the coupling between different processes or phenomena is neglected. Therefore, another optimum model could be found while using another methods of performing the paramedic study.
In addition, the comparison between the two combinations of different model parameters that present the highest and the lowest ARDs% revealed that the model deviation could be reduced by 18% and 4% for the liquid temperature and liquid CO2 loading profiles, respectively.
This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.
List of correlations used for the calculation of physicochemical and transport properties.
Summary of kinetic models for CO2 reacting with loaded aqueous MEA solution.
Summary of the enhancement factor models.
Summary of heat of absorption correlations and fixed values.
Summary of water vapor-pressure correlations.
A list of CO2 diffusivity correlations in aqueous MEA solution.
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|In the text|
Simulation results obtained using different approaches of heat of absorption: (a) Kohl and Nielsen (1997), (b) Pandya (1983), (c) Kim (2009), (d) Llano-Restrepo and Araujo-Lopez (2015) based on Kim and Svendsen (2007) data and (e) Llano-Restrepo and Araujo-Lopez (2015) based on Arcis et al. (2011) data.
|In the text|
|In the text|
|In the text|