“As discussed previously, the properties of hypothetical water-lean solvents can be calculated using parameters such as molar mass, density, and viscosity (for the Wilke–Chang correlation), Henry’s coefficient, and dielectric diffusivity. A compilation of these properties for this set of solvents can be found in Section S1. The Wilke–Chang correlation is applied to the whole solvent, i.e., considering individually the properties of the pure diluent along with the properties of MEA and treating them with a mixing rule. Henry’s coefficient of CO2 in the solvent is also calculated with a mixing rule. The rules we have employed in this study can be found in Section S2.
We have calculated the average mass transfer coefficient kg* for a set of hypothetical water-lean solvents based on 30 wt % MEA considering CO2 absorption in a column where pA in equilibrium with the loaded solvent varies between 0 and 1000 Pa, ΔpA = 100 Pa, and τ = 0.05 s. The enhancement of kg* when compared to aqueous 30 wt % MEA is shown in Table 3 for case A. Figure 4 shows the mass transfer coefficient profiles obtained for the case A analyses as a function of the CO2 partial pressure in the column.
Table 3. Comparison between Mass Transfer Coefficients in Hypothetical Solvents Based on 30 wt % MEAa
name
kg* evaluated at pA = 500 Pa
kg* averaged from pA = 0–1000 Pa
acetone
1.09 × 10–6
1.62 × 10–6
benzaldehyde
7.17 × 10–7
1.04 × 10–6
butanol
4.89 × 10–7
6.97 × 10–7
2-butanol
4.29 × 10–7
6.13 × 10–7
tert-butyl alcohol
4.05 × 10–7
5.84 × 10–7
cycloheptanone
6.06 × 10–7
8.92 × 10–7
cyclohexanol
2.56 × 10–7
3.52 × 10–7
cyclohexanone
6.30 × 10–7
9.14 × 10–7
cyclopentanone
7.00 × 10–7
1.04 × 10–6
dimethyl sulfoxide
1.07 × 10–6
1.27 × 10–6
dimethylformamide
1.28 × 10–6
1.61 × 10–6
ethanol
7.77 × 10–7
1.07 × 10–6
ethylene chloride
7.96 × 10–7
1.22 × 10–6
ethylene glycol
2.99 × 10–7
3.51 × 10–7
N-formyl morpholine
5.06 × 10–7
7.12 × 10–7
glycerol
8.84 × 10–9
1.01 × 10–8
heptanol
4.50 × 10–7
6.54 × 10–7
hexanol
4.62 × 10–7
6.70 × 10–7
isoamyl alcohol
4.32 × 10–7
6.28 × 10–7
isobutyl alcohol
4.40 × 10–7
6.26 × 10–7
isopropyl alcohol
5.14 × 10–7
7.25 × 10–7
methanol
1.27 × 10–6
1.67 × 10–6
methyl ethyl ketone
9.85 × 10–7
1.47 × 10–6
nitrobenzene
9.60 × 10–7
1.20 × 10–6
N-methyl-2-pyrrolidone
1.09 × 10–6
1.40 × 10–6
pentanol
4.42 × 10–7
6.37 × 10–7
phenyl acetonitrile
5.98 × 10–7
8.57 × 10–7
propanol
5.59 × 10–7
7.81 × 10–7
propionitrile
1.29 × 10–6
1.74 × 10–6
propylene carbonate
1.24 × 10–6
1.43 × 10–6
pyridine
7.04 × 10–7
1.05 × 10–6
sulfolane
7.29 × 10–7
8.60 × 10–7
water
9.23 × 10–7
1.06 × 10–6
a
pA = 0–1000 Pa, ΔpA = 100 Pa, τ = 0.05 s.
In Table 3, two distinct classes of results can be observed. The first class is that of water-lean solvents that would not deliver enhanced mass transfer rates when compared to aqueous solvents. This comprises any alcohol heavier than methanol, along with some diluents such as ethylene glycol, N-formyl morpholine, and sulfolane. It is interesting to stress these latter three because they are often considered promising components for water-lean solvent formulations. As it turns out, even though N-formyl morpholine and sulfolane are both good physical solvents for CO2 absorption, their high viscosity results in that one could hardly expect the mass transfer rates in mixtures between these chemicals and 30 wt % MEA to increase due to enhanced CO2 solubility. A second class of solvents can hypothetically deliver enhanced mass transfer rates. This class includes methanol, acetone, N-methyl-2-pyrrolidone, and propylene carbonate.
Figure 4 shows the mass transfer coefficient profiles for some of the hypothetical solvents calculated using the case A analysis. These solvents are divided into series of alcohols, ketones, and miscellaneous organic diluents. Some interesting facts can be observed in our results. First, it is remarkable that the best-performing organic diluents tend to be very volatile compounds (methanol, ethanol, acetone, methyl ethyl ketone). This is due to these solvents usually having very low viscosity. There is no clear relationship in the literature between viscosity and volatility, but it can be argued that these two parameters are somehow connected by electrostatic phenomena. Viscosity is an important parameter, being fundamental in explaining the results for the miscellaneous organic compounds seen in the bottom part of Figure 4. This series of solvents offers good alternatives in case one is interested in diluents with low volatility for reducing reboiler duties (see Section 4), though they are more susceptible to loss in CO2 capture capacity due to thermal phenomena in the absorber (see Section 3).
Finally, it is important to mention that, although Figure 4 shows kg* in terms of CO2 partial pressures in the column, the fact that all organic solvents analyzed have lower ε than water implies that no hypothetical water-lean solvent formulation with 30 wt % MEA will achieve a rich loading as high as that of the aqueous solvent. To illustrate this, Figure 5 shows the mass transfer coefficient profiles for the miscellaneous series of organic diluents as a function of CO2 loading. Due to their low dielectric permittivity, the decrease in mass transfer coefficients with loading observed even for promising alternatives such as N-methyl-2-pyrrolidone and propylene carbonate is steeper in water-lean solvents than in aqueous amines.
The behaviors observed in Figure 5 are strikingly similar to the ones obtained experimentally by Yuan and Rochelle (9,44) and by Wanderley et al. (14) Other than these studies, no other publications were found dealing with the mass transfer rates in loaded water-lean solvents, making any comparison with literature data even more complicated. Moreover, comparisons with the data from Wanderley et al. (14) are difficult because of the particularities of some of these solvents that go beyond this parametric analysis (e.g., sulfolane along with MEA presents phase separation upon CO2 absorption, (45) and NMP possibly reacts with CO2 directly (46)). Nevertheless, the trends resulting from our simulations are indicative that their approach is correct.
The decrease in maximum attainable rich loading in water-lean solvents is a fact that is important to keep in mind. Because of that, the only alternative for operating the amine scrubber with the same amount of solvent used in the aqueous process is by similarly reducing the lean loadings. As will be discussed in Section 4.3, this is equally problematic.
Our approach also enables the calculation of mass transfer coefficients for water-lean solvents containing mixtures of water and organic diluents. Figure 6 shows how the kg* of a sulfolane-based water-lean solvent with 30 wt % MEA would vary with the addition of water to the diluent according to our simulation results. Although the profile shown in Figure 6 has a clear inflection, its maximum does not stray too far away from the mass transfer coefficients of either aqueous or nonaqueous 30 wt % MEA individually. In theory, therefore, this allows the possibility of preparing water-lean solvents with specific proportions of water so as to tune for a better overall performance in the CO2 capture cycle.
A parametric analysis of which variables most influence kg* of different solvents is shown in the Supporting Information. The takeaway of the study is that the most important parameters are CO2 diffusivity and solubility, with dielectric permittivity being a relevant secondary parameter if case B is considered, but less so in case A studies. This is an unsurprising result and suggests that we can condense our parameter investigation into three main variables: CO2 solubility, viscosity, and dielectric permittivity. This investigation is carried out in Section 2.8.”
We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept All”, you consent to the use of ALL the cookies. However, you may visit "Cookie Settings" to provide a controlled consent.
This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.
Cookie
Duration
Description
cookielawinfo-checkbox-analytics
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checkbox-functional
11 months
The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checkbox-necessary
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-others
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-performance
11 months
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy
11 months
The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.
Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.
Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.
Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.