An Adsorbent Performance Indicator as a First Step Evaluation of

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An Adsorbent Performance Indicator as a First Step Evaluation of Novel Sorbents for Gas Separations: Application to Metal−Organic Frameworks Andrew D. Wiersum,† Jong-San Chang,‡ Christian Serre,§ and Philip L. Llewellyn*,† †

Laboratoire MADIREL, Aix-Marseille Université − CNRS UMR7246, Centre de St-Jérôme, Avenue Escadrille Normandie-Niemen, Marseille 13397 cedex 20, France ‡ Research Group for Nanocatalyst, Biorefinery Research Center, Korea Research Institute of Chemical Technology (KRICT), P.O. Box 107, Yusung, Daejeon 305-600, Korea § Institut Lavoisier, Université de Versailles St-Quentin en Yvelines − CNRS UMR8180, 45 av. des Etats-Unis, 78035 Versailles, France S Supporting Information *

ABSTRACT: An adsorbent performance indicator (API) is proposed in an effort to initially highlight porous materials of potential interest for PSA separation processes. This expression takes into account working capacities, selectivities, and adsorption energies and additionally uses weighting factors to reflect the specific requirements of a given process. To demonstrate the applicability of the API, we have performed the adsorption of carbon dioxide and methane at room temperature on a number of metal−organic frameworks, a zeolite and a molecular sieve carbon. The API is calculated for two different CO2/CH4 separation case scenarios: “bulk separation” and “natural gas purification”. This comparison highlights how the API can be more versatile than previously proposed comparison factors for an initial indication of potential adsorbent performance.

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INTRODUCTION

pressure, thereby reducing the cost of compressing the CO2 to supercritical pressure for transport and sequestration.6 For these applications, the number of potential adsorbents grows ever larger with much research devoted to developing new materials including MOFs, ZIFs, porous organic cages, polymers, and so forth,4−9 as well as improving the more traditional adsorbents such as activated carbons, zeolites, and organo-silicas.10,11 The result is a wide range of materials with greatly varying characteristics (pore size, surface area, surface chemistry, etc.) and hence very different adsorption properties (selectivity, capacity, and isotherm shape), making it difficult to compare them for a given application. It is therefore important first to be able to screen a large number of very different materials, often only initially available in limited quantities, and second to have a simple method for evaluating and comparing the merits of these materials based on readily available adsorption data, to make an initial selection of adsorbents on which to perform more extensive modeling and/or pilot scale testing. In response to the first point, a number of novel experimental systems have been developed in recent years which allow the

The separation of gases on a large scale is an essential part of many industrial processes in particular for, but not limited to, the petrochemical industry. Traditional techniques such as cryogenic distillation or solvent extraction are widespread but present several major drawbacks such as the high energy requirements and cost of installations.1 The use of corrosive and/or toxic solvents also makes many of these processes environmentally unfriendly, and there is therefore both an economic and ecological drive to find alternative technologies. The use of porous solids in processes such as pressure swing adsorption (PSA) and temperature swing adsorption (TSA) is considered by many to be a viable alternative, and indeed there are already numerous examples of commercial PSA and TSA gas separation/purification processes such as air fractionation, hydrogen production, and VOC removal to name but a few.2 The carbon capture and storage (CCS) drive has resulted in a fair amount of current research effort being devoted to the recovery of CO2 under various conditions. Relevant processes include the recovery of carbon dioxide from flue gas, the purification of natural gas, and the removal of CO2 during the production of syngas.3−5 For these, a PSA type solution is of great interest because of the additional benefit of being able to produce relatively pure carbon dioxide at above atmospheric © XXXX American Chemical Society

Received: November 7, 2012 Revised: January 9, 2013

A

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for the relative importance of selectivity, working capacity, and ease of regeneration in a process. Its applicability is demonstrated through the comparison of seven MOFs with two reference adsorbents (a zeolite and a molecular sieve carbon) for two CO2/CH4 separation processes using both Rege and Yang’s PSA selection parameter and our proposed expression. Reasoning behind the Development of the Adsorbent Performance Indicator (API). There are many criteria to take into account when comparing adsorbents. First of all, one should consider whether the materials are capable of achieving the desired separation, that is, whether they are selective toward one or other component in the mixture. Next, one can look at very basic parameters such as capacity and regeneration which will directly influence their performance in an idealized process. At this stage, it may be of interest to start looking at the impact of other factors such as kinetics or the presence of impurities on the performance of the most promising materials. Once a limited number of credible candidates have been highlighted, a more thorough investigation can be carried out using process modeling and/or pilot scale testing, based on which a meaningful comparison of the overall costs of the process can be established. The aim of the API proposed here is to allow an easy comparison of adsorbents for a given separation at an early stage and facilitate the identification of the most promising materials for further testing with regard to impurities, kinetics, and so forth. For this reason, it includes only a limited number of these criteria. As mentioned above, the first, and often the most important, is the selectivity. If the material is not selective, there will be no separation. The second is the uptake or capacity, which will determine the size of the unit that is required for the separation and hence the capital cost of the installation. However, this is dependent on the volume of adsorbent required, and therefore, it is better to compare the materials based on their volumetric uptake. This will of course be dependent on the packing density of the shaped material; however, for a preliminary study, the crystallographic density may be used. In addition, for a PSA process, one should not consider the total bed capacity but the working capacity. This will depend on the operating conditions of the process, notably the upper and lower pressures. A third factor of interest, in particular in cases where the separation is very easy or where thermal effects are likely to be important, is the heat of adsorption. The adsorption of gas is an exothermic reaction and the adsorption enthalpy a direct measure of the amount of heat generated during adsorption and required for regeneration. Because PSA processes usually operate under nonisothermal conditions, the heat released upon adsorption leads to an increase in the column temperature, which is detrimental to adsorption. Similarly, during the desorption step the column temperature decreases, which hinders the regeneration of the adsorbent. Thus, although high adsorption energies are associated with high selectivities, it is generally desirable for the strongly adsorbed component to have a relatively low adsorption enthalpy. Indeed, in cases where the adsorption enthalpy is very high, it can be difficult to remove the adsorbed molecules without additional heating of the column. The ideal adsorbent has a high selectivity, a high capacity, and a low adsorption enthalpy; however, in practice, it is very rare to find a material which combines all of these attributes. Therefore, the selection of the adsorbent(s) will often involve a

rapid determination of porosity, adsorption capacities or isotherms on multiple adsorbents simultaneously.12−14 Computational studies have also been carried out on hypothetical materials in an attempt to discover new materials with potentially interesting adsorption properties.15,16 It is therefore possible to obtain relatively easily the adsorption capacities, selectivities, and, with only a little more effort, heats of adsorption for a large number of materials. Regarding the second point, various ways of comparing adsorbents using just the pure component isotherms have been suggested over the years.17−21 Knaebel17 proposed a simple method for estimating the selectivity by taking the ratio of Henry’s law constants, which can be used as a neat but crude selection parameter. Ackley et al.18,19 compared a number of adsorbents for air separation using the product of working capacity and an adiabatic separation factor, defined as the ratio of working capacities determined under nonisothermal, multicomponent conditions. Notaro and Baksh20 put forward in a patent the “Adsorption Figure of Merit” for the separation of nitrogen and oxygen by adsorption, a separation which requires both a high capacity because of the volumes of gas to be separated, and a good selectivity because of the difficulty of the separation: AFM = WC1

αads 2 αdes

where WC1 is the working capacity of the most adsorbed component and αads and αdes are the selectivities during adsorption and desorption. This parameter was developed to help in the selection of the best adsorbent for the different sections of a multicomponent bed, and the reasoning behind it was not explained; however, it would seem to be an empirical rule of thumb. Recognizing the need for a more general parameter, Rege and Yang21 proposed the PSA selection parameter, “a simple parameter for comparing two adsorbents for a particular binary gas separation on the basis of their equilibrium adsorption capacities”: S=

WC1 α12 WC2

where WC1 and WC2 are the working capacities of the most and least adsorbed components, respectively, and α12 is the selectivity of component 1 over component 2. The two important attributes of this parameter highlighted by the authors are that it is readily estimable without complicated calculations, and it incorporates the nature of the isotherms under the PSA operating conditions. The latter is accounted for through the working capacities, defined as the difference between the amount taken up at the adsorption (high) pressure and the amount remaining in the bed at the desorption (low) pressure of the process. In their work, the authors kept the calculations very simple by assuming Langmuir behavior for all the isotherms and using the extended Langmuir theory to estimate selectivities, although other models were allowed for. While this selection parameter is simple and effective in what it aims to accomplish, a potential drawback is that it cannot be adapted to account for differing objectives in a given process, for example, whether the principal requirement is high purity or bulk separation. For this reason, we propose a new adsorbent performance indicator (API) which can be adjusted to account B

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Table 1. Feed Compositions and Operating Conditions for Two CO2 Separation Processes case 1 case 2

description

mixture

adsorption pressure (CO2 partial pressure)

desorption pressure

removal of CO2 impurities from natural gas: Purification bulk CO2/CH4 separation from acid gas

5% CO2/CH4 50% CO2/CH4

80 bar (4 bar) 80 bar (40 bar)

1 bar 1 bar

Table 2. Properties of the Different Adsorbents Used in This Study origina

material NaX Takeda 5A CuBTC UiO-66(Zr) UiO-66(Zr)-NH2 MIL-125(Ti) MIL-125(Ti)-NH2 MIL-100(Fe) MIL-101(Cr) a

Aldrich Takeda Chemical Industries Ltd. KRICT ILV ILV KRICT/ILV ILV KRICT KRICT

activation temperature, °C

BET surface area, m2.g−1

pore volume, cm3.g−1

350 150

710 1180

0.25 0.46

Atlas of Zeolite Structure Types

150 250 150 200 150 250 250

1850 970 1080 1820 1360 2410 3870

0.67 0.36 0.40 0.67 0.50 0.99 1.57

27 28 29 30 31 32 33

ref

ILV = Institut Lavoisier Versailles; KRICT = Korea Research Institute of Chemical Technology.

properties can be readily obtained for a large number of materials even at the discovery stage. The working capacity, although ideally taken from adsorption and desorption isotherms under mixture conditions, can initially be calculated from the pure component isotherms. Selectivities can also be predicted from these using models such as the extended Langmuir model,24 ideal adsorbed solution theory,25 vacancy solution theory,26 or any of their derivatives, while the heats of adsorption can be measured either directly by microcalorimetry or calculated from adsorption isotherms at different temperatures using the Clausius−Clapeyron equation. The exponents can be chosen arbitrarily for a particular separation or could be the result of extensive testing on a given setup and give the API a great versatility that makes it applicable to a wide variety of PSA processes. To illustrate this versatility, the API has been used to compare a variety of adsorbents for two CO2/CH4 separation processes presented in Table 1. Rege and Yang’s PSA selection parameter has also been calculated for each process and the results compared.

compromise between two or more of these factors, and this makes the comparison between adsorbents more difficult. For a thorough investigation of the best adsorbent for a given process, it is possible to compare their breakthrough characteristics based on the dimensionless breakthrough time;22,23 however, this involves a certain amount of process modeling. For an initial evaluation, it is more useful to have a simpler parameter. For this purpose, an “adsorbent performance indicator” is proposed which takes into account the principal selection criteria and reflects the compromise made: API =

(α12 − 1)A WC1B |ΔHads,1|C

This indicator contains the three main parameters discussed previously, with some adjustments. The adsorption enthalpy of the most adsorbed species ΔHads,1 is in the denominator because the heat generated during adsorption is detrimental to the performance of the process. The selectivity has been included as (α12 − 1) rather than just α12. This serves a dual purpose: in the first place, it enhances the differences in selectivities close to 1, and in the case that the material is unselective, a value of 0 is obtained for the API. Second, the indicator calculated for a material which is selective toward component 2 will give negative values, immediately highlighting the inversion in selectivity. While such a material is potentially of great interest for the separation process, it is illogical to try and compare it with materials which are selective toward component 1. It should also be noted that this modification will have very little impact in cases where the selectivities are far from 1. Finally, in order to be able to adapt the API to each separation process, exponents were added to each factor so as to be able to adjust the relative importance of each. Indeed, one can imagine for bulk separations that the working capacity is of greater importance, whereas for purifications, with small amounts of component 1 to be removed, α12 is of prime interest. By default, all the exponents are set to 1 and they can then be adjusted based on the objectives of the process. This new indicator comprises three adsorbent properties (selectivity, working capacity, and adsorption enthalpy) and three exponents which are based on the process. The adsorbent

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EXPERIMENTAL METHODS

Samples. Adsorption experiments have been carried out on nine different adsorbents: seven MOFs, one zeolite, and one molecular sieve carbon. The MOFs were kindly provided by the Institut Lavoisier in Versailles (ILV), France, and the Korea Research Institute of Chemical Technology (KRICT) in Deajeon, South Korea. The zeolite and the molecular sieve carbon used as reference adsorbents were obtained commercially. The main properties of all these materials are listed in Table 2, with additional characteristics available in the Supporting Information. Gravimetry. The excess adsorption isotherms for pure CO2 and CH4 were obtained gravimetrically using a commercial device (Rubotherm Präzisionsmeßtechnik GmbH).34 Approximately 1 g of dried sample was used for these experiments. Samples were activated in situ by heating under vacuum to their activation temperature (see Table 2) for 16 h. The experiments were carried out at 303 K up to a maximum pressure of 100 bar for CH4 and 60 bar for CO2. The gas was introduced using a step-by-step method, and equilibrium was assumed to have been reached when the variation of weight remained below 30 μg over a 15 min interval. The volume of the sample was determined from a blank experiment with helium as the nonadsorbing gas and used in combination with the gas density measured by the Rubotherm balance to compensate for buoyancy. Each experiment C

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consisted of two adsorption−desorption cycles with a 2 h vacuum in between to ensure complete regeneration and reproducibility. The high purity gases were obtained from Air Liquide: N55 quality (99.9995% purity) for CH4 and N45 quality (99.995% purity) for CO2. Fitted Equations. The experimental pure component isotherms were fitted with a variety of equations listed in Table 3, which were

Table 3. Isotherm Equations Used to Fit Pure Component Experimental Data isotherm equation Langmuir multisite Langmuir Toth Jensen−Seaton

bP na = nma 1 + bP a na = nm,1

na = nma

b1P b2 P + nma ,2 + ... 1 + b1P 1 + b2 P

bP (1 + (bP)t )1/ t

−1/ c ⎡ ⎛ ⎞c ⎤ KP na = KP ⎢1 + ⎜ ⎟⎥ ⎢⎣ ⎝ a(1 + κP) ⎠ ⎥⎦

ref 35 35 36 37

subsequently used to represent the experimental pure component data in the mixture predictions. Binary selectivities were predicted using the ideal adsorbed solution theory (IAST).25 Because IAST calculations are heavily dependent on the accuracy of the fit in the low pressure region, the different sets of parameters were determined by minimizing the sum of the relative difference between experimental and theoretical values rather than just the absolute difference, in order to ensure the fit was particularly good at low pressure. For each material the fitting equation providing the best fit for both components was used to predict the selectivity. Where the fits were equally good, the simplest equation (with the least adjustable parameters) was chosen. Microcalorimetry. Adsorption enthalpies were measured experimentally at 303 K using a Tian−Calvet type microcalorimeter coupled with a homemade manometric gas dosing system.38 This apparatus allows the simultaneous measurement of the adsorption isotherm and corresponding differential enthalpies. Approximately 0.5 g of dried sample was used for these experiments. Samples were activated ex situ by heating under secondary vacuum to their activation temperature (see Table 2) using sample controlled thermal analysis for 16 h.39 The dead-space volume and heat effects due to gas compression were estimated from a blank experiment with helium.

Figure 1. Adsorption isotherms for carbon dioxide (a) and methane (b) at 30 °C.

CH4 selectivities of all the adsorbents were therefore predicted using the IAS theory, which has been shown to give accurate predictions for CO2/CH4 selectivities in MOFs and zeolites.44−46 Estimation of Selectivities Using IAST. In order to apply IAST, the pure component isotherms need to be fitted with a model equation. In the case of methane, all the isotherms could satisfactorily be fitted with a simple Langmuir equation and the more complicated models did not lead to a significantly better fit to the experimental data. In the case of carbon dioxide, the isotherms differed substantially in shape from one adsorbent to the next and more complex isotherm equations such as triple site Langmuir, Toth, and Jensen−Seaton were sometimes required to get an adequate fit to the experimental data. Examples of this as well as the fitted parameters for all the different models, gases and MOFs can be found in the Supporting Information. Using the most accurate set of fitted parameters in each case, the IAST was applied and the selectivities of all the materials for the two separations from Table 1 were calculated and can be found in Table 4. As expected from the pure component isotherms, all the materials are selective toward CO2. The zeolite NaX has the highest CO2 selectivity of all the adsorbents tested during this study, the molecular sieve carbon Takeda 5A has the lowest, and the MOFs were found to have a selectivity marginally if not substantially higher than the carbon but

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RESULTS Pure Component Isotherms. The pure component adsorption isotherms for CO2 and CH4 on the different adsorbents are presented in Figure 1. All the isotherms were fully reversible with no hysteresis, and the second adsorption experiment overlapped the first, indicating full regeneration under primary vacuum. All the MOFs investigated here adsorb more CO2 per gram of material than the reference zeolite NaX, and many also outperform the molecular sieve carbon Takeda 5A, indicating that they are potentially very interesting for the removal of CO2. In particular, the mesoporous MIL-101(Cr) adsorbs in excess of 30 mmol·g−1 at 50 bar, giving it one of the highest capacities for CO2 in the literature.40 Others such as CuBTC and MIL-100(Fe) have also been widely studied for their CO2 capture and separation potential.41−43 The variety of isotherm shapes obtained means that it should be possible to find adsorbents suited to processes with a wide range of operating pressures. In all cases, the amount of CO2 adsorbed is significantly higher than that of CH4, indicating that all the materials are selective toward carbon dioxide. The binary CO2/ D

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Table 4. CO2 Adsorption Enthalpies and Selectivities of All the Materials for the Two CO2/CH4 Separation Processes, Predicted from IAST |ΔHads,CO2| material NaX Takeda 5A CuBTC UiO-66(Zr) UiO-66(Zr)-NH2 MIL-125(Ti) MIL-125(Ti)-NH2 MIL-100(Fe) MIL-101(Cr)

αCO2/CH4

−1

kJ·mol 49 35 30 27 35 26 30 37 59

→ 38 → 26

→ 31

→ 22 → 22

case 1: purification

case 2: bulk separation

198 3.7 7.1 3.9 8.1 3.8 5.3 19.6 23.6

148 3.8 7.5 4.1 8.4 4.3 6.1 13.8 12.0

considerably lower than the zeolite. It is interesting to note the increased selectivity of UiO-66(Zr)-NH2 and MIL-125(Ti)NH2 compared to their unfunctionalized counterparts, which has been reported previously16 and illustrates the possibility in MOFs to modify the surface chemistry in order to enhance adsorption properties. Heats of Adsorption. The adsorption enthalpies for CO2 on all the materials were measured experimentally, and the results have been summarized in Table 4. The heats of adsorption for CuBTC, UiO-66(Zr), MIL-125(Ti), and MIL125(Ti)-NH2 remain more or less stable over the pressure range investigated, suggesting relatively homogeneous adsorption sites (although this turns out not to be the case for CuBTC),47 while MIL-100(Fe), MIL-101(Cr), and NaX have significantly higher initial adsorption enthalpies which decrease with increasing coverage, indicating a distribution of adsorption sites of differing strengths. This will not be discussed in any more detail here as it has been studied previously;48 however, it is interesting to observe that these stronger adsorption sites lead to higher CO2 selectivies. Overall, NaX has the highest adsorption enthalpy and the molecular sieve carbon Takeda 5A one of the lowest. Again, one can note the increased interaction between CO2 and the amine-functionalized UiO-66(Zr) and MIL-125(Ti) materials.

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DISCUSSION The binary CO2/CH4 selectivities of the different materials investigated in this study have already been discussed, and they show that MOFs seem to have on the whole intermediate properties between zeolites and activated carbons as far as CO2 selectivity is concerned. As mentioned above, in a process with a given bed volume, it is the uptake per volume of adsorbent which is the more relevant parameter, rather than the usual isotherm representation of uptake per gram of sample. Figure 2 shows the CO2 adsorption isotherms for all the materials on a mass basis (a) and a volumetric basis (b). Interestingly, while the MOFs generally had a higher CO2 capacity per unit mass of sample than the zeolite and the molecular sieve carbon, once the density is taken into account, their capacity is lower. The working capacities (per unit volume of sample) corresponding to the two separation cases can be found in Table 5. As previously discussed, these have been estimated from the pure component adsorption isotherms taking into account the partial pressure of each gas in the mixture. It should be noted that the amount remaining in the bed upon regeneration should be taken from the desorption isotherm;

Figure 2. CO2 adsorption isotherms for all materials on a mass basis (a) and volume basis (b).

however, in this case, all the isotherms are fully reversible with no hysteresis at least down to 1 bar and therefore either can be used. It turns out that, because of the different shapes of the CO2 isotherms, the working capacities can be very different from the total capacities. While NaX has one of the highest capacities on a volumetric basis, the bulk of the adsorption takes place below 1 bar, making it rather less interesting for the two cases being considered here. The molecular sieve carbon, by virtue of having a fairly smooth adsorption isotherm slope, retains a high capacity over the whole range of pressures. Regarding MOFs, E

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Table 5. Working Capacities and Average Adsorption Enthalpies for the Two CO2/CH4 Separations Presented in Table 1 case 1: purification

case 2: bulk separation

material

WCCO2, cm3 (STP)·cm−3

WCCH4, cm3 (STP)·cm−3

|ΔHads,CO2|, kJ·mol−1

WCCO2, cm3 (STP)·cm−3

WCCH4, cm3 (STP)·cm−3

|ΔHads,CO2|, kJ·mol−1

NaX Takeda 5A CuBTC UiO-66(Zr) UiO-66(Zr)-NH2 MIL-125(Ti) MIL-125(Ti)-NH2 MIL-100(Fe) MIL-101(Cr)

40 90 125 61 71 71 69 47 38

204 248 241 163 176 138 122 116 125

38 26.6 29.3 26.6 30.6 25.5 29.8 24.7 24.5

94 293 218 180 168 179 142 221 258

189 223 201 136 153 127 117 99 98

38 27.1 30.5 27.4 30.6 27.6 31.2 22.8 23

sacrifice some selectivity in order to have a very high capacity. For this reason, the exponents for the selectivity (A) and the working capacity (B) have been set to 0.5 and 2, respectively. These values have been selected entirely arbitrarily; however, there is plenty of scope to fine-tune them for a specific process based on past data or modeling. The purpose of these exponents is precisely to make the API as versatile as possible. Figure 3 shows the PSA selection parameter and the API values calculated for each material for the two case studies. For both indicators, the most promising adsorbent corresponds to the material with the highest calculated value. In the first case, the general trend regarding the comparison of the different materials based on the two indicators is very similar. Both the PSA selection parameter and our indicator suggest that NaX is the preferred adsorbent and is likely to perform significantly better than either the molecular sieve carbon or the MOFs studied in this work. This is reasonable considering the vast differences in selectivities observed earlier. In the case of the bulk separation (case 2), the API clearly favors the materials with the highest working capacities while Rege and Yang’s PSA separation parameter still suggests that the zeolite would be the most appropriate adsorbent. This shows the limitation of the PSA selection parameter, which may not be the most adaptable to reflect the principal aim of this process. In reality, the energy required to remove large quantities of CO2 and the difficulties in doing so, as well as the lower capacity of the zeolite, mean that zeolites are far less likely to be used for this type of bulk separation. This is in line with common practices in industry, where a number of patents can be found relating to the separation of CO2 from natural gas using high capacity adsorbents such as activated carbons.49−52 Based on the API, it would appear that the two mesoporous MOFs (MIL-100(Fe) and MIL-101(Cr)) could be at least as good if not better than the molecular sieve carbon presented here, and would be worth investigating further. It should be noted that in both cases the selection parameters do not take into account the influence of impurities, which can play an important role. NaX is particularly sensitive to the presence of water in the mixture, which substantially reduces its selectivity towards carbon dioxide and therefore greatly reduces its performance, which is not the case of Takeda-5A. The adsorption behavior of MOFs in the presence of water has only recently started to be investigated and this has revealed some interesting results.53−55 It is known that certain MOFs such as CuBTC and MOF-5 are not stable with regard to water and suffer a loss of crystallinity when exposed to humidity which significantly decreases their adsorption capacity. The adsorption of carbon dioxide in the UiO-66 materials, which are renowned

the pressure range is critical. MIL-101(Cr), for example, has the highest capacity of any MOF studied here between CO2 partial pressures of 1 and 40 bar but has the lowest when considering an uptake to 4 bar. CuBTC on the other hand has an average working capacity between 1 and 40 bar but is significantly better than all the other adsorbents between 1 and 4 bar. Overall, the working capacity of MOFs is generally better than that of NaX but often lower than that of the molecular sieve carbon. In the calculation of the API, the energy of adsorption of the most strongly adsorbed component is taken into account as it is this that will define the ease of desorption as well as the extent of any thermal effects in the column. In this case, the integral adsorption enthalpies have been calculated over the range of loadings corresponding to the partial pressures of carbon dioxide in the mixture in order to have the average heat of adsorption for the process. These can be found in Table 5. The adsorbent performance indicator and Rege and Yang’s PSA selection parameter have been calculated for each adsorbent for the two generic cases presented earlier using the selectivities in Table 4 and the working capacities and adsorption enthalpies from Table 5. The exponents selected for each case are shown in Table 6. Table 6. API Exponents A, B, and C Selected for the Two Case Studies reasoning case 1: purification case 2: bulk separation

This corresponds to the removal of CO2 impurities from methane. Selectivity and working capacity are both important. This is a bulk removal of CO2 from CH4 with a second purification step downstream. The most important factor is working capacity.

A

B

C

1

1

1

0.5

2

1

The reasoning behind these choices is relatively straightforward. In case 1, the objective is to obtain a high purity and recovery rate for methane therefore the selectivity is an important factor. Although the CO2 mole fraction in the feed is low, the separation is likely to be carried out on a very large scale; therefore, a good working capacity is also required. In this case, therefore, there is no need to adjust the relative importance of any of the parameters and the default values of 1 can be used for all three exponents. In case 2, the fraction of CO2 is much greater and the primary aim is a bulk removal. The purity requirements are potentially not as stringent, as there is the possibility of having a second separation unit downstream; therefore, one is willing to F

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selectivity;43,56−58 however, generally the materials with higher selectivities have unsaturated metal coordination sites which make regeneration more difficult, while for the other materials the selectivities remain close to 1. In the first case, the exponent C can again be increased to highlight materials with a weaker propylene−adsorbent interaction, while in the latter case the proposed API in its current form helps to emphasize the difference in selectivities between the different materials, and the exponent A can be adjusted to increase further the importance of selectivity in the evaluation of potential adsorbents. Finally, although the purpose of the API is to provide a first step evaluation of adsorbents for a given gas separation based on readily available data, there is the potential to build upon the current expression to include other parameters relevant to a PSA process such as diffusivities in order to have a more detailed comparison of the materials; however, this adds a level of complexity as well as requiring more data and is possibly more in the remit of process modeling.

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CONCLUSION It is not always a simple task to compare adsorbents of different natures for a given separation process, and this work has given two extremes with a highly selective zeolite and a molecular sieve carbon with high working capacity. The adsorption performance indicator (API) put forward here provides a fairly simple but flexible parameter to compare the aforementioned materials with a number of metal−organic frameworks. Nevertheless, this approach is easily applicable to adsorbents of any nature. The flexibility of the API arises from the weighting factors which provide the possibility to direct this function toward different process requirements as highlighted here with the necessity of high working capacity in bulk separations or the need for high selectivities in the case of the removal of trace quantities of gas. The simplicity arises from the use of readily calculable data including working capacity, selectivity, and adsorption energies which can be obtained from lab-scale experiments without the need for pilot testing. Indeed, it could equally be applied in conjunction with data obtained from GCMC simulations. Furthermore, one can envisage that this factor be extended to include other parameters; both physically relevant to the process (Cp, diffusivity, etc.) or of wider interest such as stability and adsorbent cost. Finally, this API can be combined with high throughput experimentation, which we are currently developing, as a screening tool to highlight novel materials of interest for a wide range of binary gas separations.

Figure 3. Light gray bars: PSA selection parameters (Rege and Yang). Dark gray bars: API (this work) values calculated for the two case studies.

for their stability, is not affected by the presence of humidity while in the case of MIL-100(Fe), the presence of water vapor in a CO2/N2 stream in fact leads to a 5-fold increase in the amount of carbon dioxide adsorbed at 1 bar (partial pressure of 0.2 bar). With regard to the comparison between the PSA selection parameter and the API, other more extreme examples could be discussed to illustrate the versatility of this new adsorbent performance indicator, for example, the separation of propylene from nitrogen. This mixture is relatively easy to separate thanks to very high selectivities for most adsorbents (>100); however, thermal effects are likely to be a problem due to the high adsorption enthalpies of propylene, and bed regeneration is also difficult, particularly in materials with small pores bearing Lewis metal sites such as CuBTC. In such a case, the exponent for the enthalpy (C) could be set to 2 or even 3 to reflect the difficulty in regeneration and the need for weak interactions. As the selectivities are all high, they should play a less important role in the selection process so the corresponding exponent (A) can be set to 0.5. Another example is the separation of propylene from propane, which is a particularly difficult separation because of the similar nature of the molecules. Several MOFs have been identified which have a slight to moderate propylene

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ASSOCIATED CONTENT

S Supporting Information *

Additional adsorbent characteristics; examples of adsorption− desorption cycles; fitted parameters for all isotherm equations for carbon dioxide and methane on all the materials; carbon dioxide adsorption enthalpies as a function of coverage. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +33 (0)4-55-18-28. Fax: +33 (0)4-55-18-50. E-mail: [email protected]. G

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS J.-S.C. is grateful to the Korea CCS R&D Center (KCRC) for project support, funded by the Korea government (Ministry of Education, Science and Technology). C.S. is also grateful for the CNRS international scientific collaboration PICS program. The present authors are grateful to U-Hwang Lee, Young Kyu Hwang, You-Kyong Seo, and Ji Sun Lee from KRICT and Florence Ragon, Vincent Guillerm, and Thomas Devic from ILV for the synthesis of MOF samples. A.D.W. and P.L.L. also thank Catherine Leroy from Total Petrochemicals France as well as Guy de Weireld and Nicolas Heymans from the University of Mons for their input and advice. The research leading to these results has received funding from the European Community’s Seventh Framework Programme (FP7/2007− 2013) under Grant Agreement No. 228862 (Macademia project).

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