Impacts of Gas Impurities from Pipeline Natural Gas on Methane

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Impacts of Gas Impurities From Pipeline Natural Gas on Methane Storage in Metal-Organic Frameworks During Long Term Cycling Ying Wu, Dai Tang, Ross J. Verploegh, Hongxia Xi, and David S. Sholl J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/acs.jpcc.7b03459 • Publication Date (Web): 29 Jun 2017 Downloaded from http://pubs.acs.org on July 5, 2017

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The Journal of Physical Chemistry

Impacts of Gas Impurities from Pipeline Natural Gas on Methane Storage in Metal-Organic Frameworks during Long Term Cycling Ying Wu1,2, Dai Tang2, Ross J. Verploegh2, Hongxia Xi1, David S. Sholl2,* 1

The School of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, Guangdong, People’s Republic of China 510641

2

School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 303320100

*CORRESPONDING AUTHOR: [email protected]

1 ACS Paragon Plus Environment


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Abstract The development of adsorbed natural gas (ANG) technology creates opportunities for use of pipeline natural gas as clean fuel in vehicles. Metal-organic frameworks (MOFs) are one class of materials that have received considerable attention as possible adsorbents in ANG applications. We examine how accumulation of trace components from pipeline natural gas will impact the performance of MOFs in ANG during long term cycling. Our approach combines information from Grand Canonical Monte Carlo (GCMC) simulations of single component adsorption, Ideal Adsorbed Solution Theory (IAST) of multicomponent adsorption, and an isothermal model of tank cycling to assess accumulation of heavy hydrocarbons and tert-butyl mercaptan (TBM). In a series of MOFs, a reduction in deliverable energy up to 50% is observed after 200 cycles. These results highlight the importance of considering multicomponent effects during consideration of adsorbents for ANG applications.


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1. Introduction The demand for high quality energy with environmental and economic sustainability is a critical global need. Total global energy production is predicted to rise by more than 20% from 2016 to 2040, led by increases in renewables and natural gas.1 With lower carbon emission and higher thermal efficiency compared to other traditional stoichiometric counterparts,2-4 natural gas has advantages as a fuel, particularly for automotive vehicles.5 However, commercial use of natural gas as a vehicular fuel is inhibited by its high storage cost, either from cryogenic vessels (with low temperatures about 111 K) in liquefied natural gas (LNG),6-8 or heavy cylindrical tanks (with high pressures up to 20 MPa9) in compressed natural gas (CNG). As an alternative, adsorbed natural gas (ANG) has attracted widespread interest, since ANG can potentially provide high energy density while reducing the storage pressure (3.5-4.0 MPa9), allowing lightweight and versatile design of vehicle fuel tanks.10-13 Despite the advantages of ANG, the commercialization of this technology is hampered by a number of challenges.14 A large number of studies have been carried out both theoretically and experimentally developing adsorbents for ANG system. Li et al.15 reported that the carbon adsorbents prepared from petroleum coke allow 105.7 v/v methane delivery capacity from 0.5-3.5 MPa at 298 K. Lozano-Castello et al.16 compared various carbon materials, e.g. chemically activated carbons (ACs), physically activated carbon fibers (ACFs), and activated carbon monoliths (ACMs), to analyze how the structural properties and packing density of the carbons affect methane adsorption, and showed that the methane delivery in activated carbons and activated carbon monoliths reach 145 v/v and 126 v/v, respectively. However, the deliverable capacity of carbon materials is still insufficient for commercial use according to the US Department of Energy’s Methane Opportunities for Vehicular Energy (MOVE) program,17 which set a target of 263 v/v deliverable capacity of methane from an ANG tank without packing loss.8



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Metal-Organic Frameworks (MOFs) have drawn widespread attention for natural gas storage at ambient conditions, due to their large adsorptive capacity and highly tunable structures.18-20 Teo et al.6 combined experiments and grand canonical Monte Carlo (GCMC) simulation to examine MIL-101(Cr) and Cu-BTC in ANG-LNG coupling condition for the adsorption of methane and CO2. Similarly, by employing ANG-LNG coupling, Kayal et al.21 reported that MIL-101(Cr) exhibits high methane deliverable capacity of 240 v/v from 6 bar at 160 K to 5 bar at 298 K. Bimbo et al.22 compared MIL101(Cr) and two carbon materials (TE7-20 and AX-21) for adsorptive delivery of methane, showing that MIL-101 outperformed the carbons. Several studies have used computational modeling to screen large collections of MOFs and other crystalline porous materials for methane storage.20, 23 Apart from adsorbent materials, thermal management is another significant issue in ANG, since the heat generated from adsorption diminishes the gas storage capacity, while the endothermic desorption reduces the system temperature, restricting the gas delivery from the tank. Biloe et al.24 used expanded natural graphite (ENG) as an adsorbent and reported that the thermal conductivity in the system was about 30 times higher than that of activated carbon packed bed, which provides a more isothermal operating condition. Mota25 and Blazek et al.26 introduced reversible phase-change materials into an adsorbent bed to transfer the heat generated from the adsorption cycle to desorption cycle. Here we focus on ANG tank for vehicular use, and assume a flow rate low enough in both filling and release operations8, 27 that allows us to assume isothermal conditions in the adsorption and desorption processes. This slow filling and release approach has been successfully employed in previous experiments to minimize thermal effects inside ANG tanks.28 The development of ANG technology also needs to consider effects due to natural gas impurities. Pipeline natural gas is a complicated mixture that is about 95% methane, but includes other components, such as ethane, propane and heavier hydrocarbons, along with trace levels (ppm) of sulfur odorants like 4 ACS Paragon Plus Environment

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tert-butyl mercaptan (TBM).8,

29

Combustion of TBM and similar sulfur-containing compounds

produces acidic SOx, leading to a variety of problems like the degradation of adsorbents.30-32 Although the amount of the impurities in pipeline natural gas is low, these components will typically adsorb more favorably in adsorbents than methane, making them more difficult to desorb and potentially obstructing adsorption sites for methane during multiple filling and release cycles.8 Sun et al.33 compared the difference between the adsorption of pure methane and the storage of natural gas (methane mixed with low levels of impurities) in various kinds of adsorbents (adsorption resin, activated carbon, polymer, etc.), suggesting that the influence of impurities on storage mechanism should be taken into account in developing ANG adsorbents. Pupier et al.34 and Li et al.15 experimentally investigated ANG tanks filled with activated carbon and observed deterioration of storage performance due to accumulation of impurities. This phenomenon has been recognized in the adsorption community for many years.14, 27, 35-38 Because of the time consuming nature of experiments with long term cycling and the challenge of analyzing trace contaminants, experimental analysis of impurity accumulation in ANG systems has been limited. Models predicting the performance of ANG systems typically simplify the composition of natural gas, making it possible to assess the long term role of impurities.8 It is therefore desirable to develop a mathematical model that can describe the role of trace impurities during long term cycling of ANG storage system. In this paper, a mathematical model is developed to simulate the performance of an ANG system for storage of pipeline natural gas with up to eight components using MOFs as adsorbents. Grand Canonical Monte Carlo (GCMC) simulations are employed to calculate the single-component isotherms, and Ideal Adsorbed Solution Theory (IAST) is then used to describe multicomponent adsorption. We report the deliverable capacity and deliverable energy using a filling pressure of 65 bar and a release pressure of



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5.8 bar at room temperature (298 K). The influences of impurities (especially heavy hydrocarbons and trace levels of TBM) as well as different MOFs on tank performance are studied in detail.

2. Theory and Modeling Methods 2.1 GCMC simulations The adsorption of methane, ethane, propane, butane, pentane, hexane, CO2 and tert-butyl-mercaptan (TBM) in a number of MOFs was simulated by Grand Canonical Monte Carlo (GCMC) calculations using the RASPA 2.0.2 molecular simulation package.39 Dispersive interactions between adsorbates and MOFs were modeled with Lennard-Jones interactions combining the TraPPE40-42 (for hydrocarbons and TBM), EPM243 (for CO2), and Universal Force Field44 (for MOF framework atoms) using LorentzBerthelot mixing rules. Electrostatic interactions were computed by Ewald summation, using TraPPE/EPM2 charges and DDEC point charges18,

45-47

for adsorbates and MOF framework atoms,

respectively. Dispersion interactions were truncated at 12.8 Å without any tail corrections, 2×2×2 or 3×3×3 unit cells of MOFs with periodic boundary conditions were used as simulation volumes to ensure the length of periodic images was more than twice the cutoff radius. The studied MOF structures along with DDEC charges were taken from Computation-Ready Experimental (CoRE) MOF Database,18-19, 23 and the MOF framework atoms were treated as rigid in all calculations. We used the united-atom TraPPE force field to describe all hydrocarbons, where the -CH3 and -CH2 groups in hydrocarbons are modeled as LJ spheres.48-51 For propane, butane, pentane, hexane and TBM, intramolecular flexibility was sampled using configurational-bias Monte Carlo (CBMC)52-54 within the TraPPE force field. Single component isotherms were calculated at 298 K at the pressures ranged from 1×10-10 bar to the saturated vapor pressures of each adsorbate. The Peng-Robinson equation of state55 was used to 6 ACS Paragon Plus Environment

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define the pressure of each species. The GCMC simulations were performed with 3×104 initialization and 5×105 production Monte Carlo cycles, during which random attempts of displacing, regrowing, rotating, inserting, or removing molecules were included. Initial testing indicated that these simulation lengths gave well converged results. In GCMC simulations of multicomponent adsorption, a mixture representative of natural pipeline gas composition (95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.7% CO2 and 0.001% TBM) was used with 5×104 initialization and 5×105 production Monte Carlo cycles. The number of Monte Carlo production cycles for multicomponent gas adsorption we used has been verified to be well converged (see Figure S2). Throughout this paper, mixture compositions were defined in a molar basis. The selectivity in adsorbed mixtures was defined by: = ∑

/∑

(1)

where x and y are the concentration of component i in adsorbed and gas phase, respectively and the summation includes all components in the mixture except for component i. Although multicomponent GCMC simulations are useful to examine multicomponent adsorption for specific state points, modeling of cyclic operations of a tank for a complex gas mixture requires a means to define multicomponent adsorption for an enormous range of conditions. To this end, we applied Ideal Adsorbed Solution Theory (IAST)56-57 to predict multicomponent adsorption from our GCMC-derived single-component isotherms in each MOF, by fitting every single-component isotherm to a Langmuir isotherm that was then used in our IAST calculations. The single-component isotherms in each MOF we considered were shown in Figure S1 and the Langmuir fitting parameters for each component were listed in Table S1.



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2.2 Tank model for cyclic adsorptive gas storage A tank model to describe cyclic adsorptive gas storage was developed, based on Zhang et al.’s8 model that considered three components (96% methane, 3.3% ethane, and 0.7% propane) in the mixture. This model assumed cyclic operation with 0.0009 L (pipeline gas)/second gas consumption rate in a 152 L ANG tank with 25% packing loss. We calculated the results (working capacity, deliverable energy and composition in the tank, etc.) from a filling pressure of 65 bar to a release pressure of 5.8 bar at 298 K, consistent with previous studies of adsorptive methane storage.8, 20 The filling (adsorption) and release (desorption) of the tank were assumed to be a relatively slow process. Under this assumption, the tank was modeled as being isothermal; pressure and concentration gradients in the tank were neglected, and instant equilibrium between the adsorbed and gas phases during filling and emptying was assumed. Although more detailed models of tank dynamics are of course possible,58-60 this isothermal equilibrium approach is sufficient for assessment of impurity effects in the MOFs that we describe below.


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Table 1. Nomenclature Symbols

Definitions

N

Number of components in the inlet flow mixture

Selectivity of component i over others in tank

Mole fraction of component i in adsorbed phase

Mole fraction of component i in gas phase

,

Fugacity coefficient of component i in gas phase Langmuir isotherm fitting parameters of single component i Total pressure in tank (bar)

Total number of molecules for all components in adsorbed phase (molecules/unit cell)

Total number of molecules for all components in gas phase (molecules/unit cell)

Avogadro constant (6.02×1023)

Volume of single MOF unit cell (m3)

Molar volume of gas phase (m3/mol)

Molar flow rate of all components released out of tank (molecules/unit cell/s)

Molar mass of each component (g/mol)

Constant mass flow rate (g/unit cell/s) Total number of molecules of component i in tank

Total number of molecules of component i in tank left from previous cycle

Time (second) Ratio between outlet and inlet molar fraction of component i !"

#$

! '

∆

Mole fraction of component i in inlet gas flow Heat of combustion of component i (kJ/mol) Deliverable capacity of component i at the end of kth cycle (molecules /unit cell)

th ∆( ! ' Deliverable energy of the tank at the end of k cycle (MJ/L of tank)



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The cyclic process in our model includes a filling (adsorption) process and release (desorption) process as one working cycle. During the adsorption process, pipeline gas was introduced into the tank, among which the gas-phase fugacity coefficient, the amount and composition of each component in the adsorbed and gas phases of the tank were simultaneously changed with the increasing total pressure until 65 bar was reached. To express these changes, a set of 3N nonlinear algebraic equations must be solved, where N was the number of components:

= )* + , , . … , ∙ log 61 + 9

)

,

?

= 9

, N equations

(2)

)

(3)

:; = < ∙ log 61 + < =

= ∑

= )* + , , . … ,

N

PQR

>; , N-1 equations

AB C 11 D AB C > E = D

:I ,

? @ O 0 PQR ? @ O 0

+,@ +

F..HIJK EM F.LH

0, 1

=

STRU

STRU

(4)

0, 1

, N-1 equations

(5)

(6)

The nomenclature used in these equations was defined in Table 1. The fugacity coefficient of each component (in Eq. 2) and the molar volume of gas phase (in Eq. 5) were represented by gas-phase composition using Peng-Robinson equation of state for mixture. Key parameters (i.e. critical temperature, critical pressure and acentric factor) of each component were listed in Table S2. was converted to the total amount of gas molecules ( ) using Eq. 5. Based on IAST, Eq. 3 and Eq. 4 defined the equilibrium between gas and adsorbed phases for each component, and the total amount of adsorbed molecules ( ), respectively. Eq. 6 reflected that the composition of newly introduced amount


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in the tank (calculated by subtracting the previously accumulated amount from the total amount in tank) was kept consistent with that of the inlet gas flow to satisfy conservation of mass. To describe the desorption process (that is, the decrease of pressure from 65 bar to 5.8 bar), 4N+2 equations must be solved, including the same 2N+1 as Eq. 2~Eq. 5, and an additional 2N+1 equations to define the flow rates: ∑ = ∙

(8)

= + , N equations V V"

= − , N equations

(9) (10)

Here we assumed a constant mass flow rate ( ) during emptying of the tank, which was computed from the gas consumption rate (0.0009 L (pipeline gas)/s) during a single release cycle, since the composition of outlet gas flow (equals the gas phase in tank) did not fluctuate dramatically. This assumption corresponded to a nearly constant energy flow rate from the tank, which matches the engine requirements of a vehicle.17 Eq. 10 defined a set of differential algebraic equations (DAEs) for the molar flow rate of each component leaving the tank. At the end of each operating cycle, we measured the deliverable capacity and deliverable energy: ! '

∆

= XH − H.Y

∆( ! ' =

KBKTR Z

∑ ∆ [ EM IJK

(11)

(12)

where the deliverable capacity at kth cycle was calculated by subtracting the amount of component i remaining in the tank at the end of release process (at 5.8 bar) from the amount of component i when



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the filling process was completed (at 65 bar). The deliverable energy of tank was the total energy of all the components delivered except CO2, as the heat of combustion for CO2 is zero. As the number of components grew, solving the equations above became numerically challenging, especially when the isotherms of the adsorbing components varied strongly. To overcome this challenge, we applied a rescaling treatment to the input parameters of each component to equalize their effects in the tank. It was not possible to obtain well converged numerical results in many cases of interest without this approach. More details related to the equations and methods of this modeling were given in the Supplementary Information.

3. Results and discussion 3.1 Modeling of pipeline gas in Cu-BTC We first discuss the adsorption properties of natural gas mixtures in Cu-BTC to illustrate the behaviors of these mixtures in MOFs. Since Cu-BTC has octahedral pockets that are only accessible through the triangle windows (the windows are ~3.5 Å in diameter61-63), we blocked these pockets with artificial spheres of 6 Å in diameter centered in these pockets in our GCMC calculations. Singlecomponent isotherms of C1 to C4 hydrocarbons in Cu-BTC were calculated by GCMC simulations, as shown in Figure 1(a), and compared to experimental data gathered by Mileo et al.64 Although there are some deviations between the experimental and simulation data, the reasonable overall agreement indicates that the force fields used in our GCMC simulations provide physically meaningful predictions. Although CO2 adsorption on Cu-BTC has been widely studied, the available experimental data shows a great deal of variation (see Figure 1(b)). Although the isotherm predicted by our GCMC calculations is broadly consistent with a significant number of reported experiments, the large variations between 12 ACS Paragon Plus Environment

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experimental reports mean that it is not possible to argue that our predicted isotherm is (or is not) accurate when compared to experiments. Figure 1(c) compares the predictions of IAST for an eightcomponent bulk mixture (95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.001% TBM and 0.7% CO2) adsorbing in Cu-BTC with multicomponent data for the same mixture from mixture GCMC simulations at total pressures ranging from 5.8 bar to 65 bar. The results show good agreement between IAST predictions and GCMC data, suggesting that the Langmuir model and the related fitting parameters we used for each adsorbate are adequate to describe the mixture adsorption behaviors in Cu-BTC. Although IAST is applied for ideal solutions on homogeneous adsorbents, it is likely to provide reasonable predictions for our examples when methane dominates the mixture, as has been seen when comparing IAST to experimental data for CO2/water mixtures on zeolite 5A with low water loading.65



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Figure 1 (a) GCMC-simulated single-component isotherms (closed symbols) compared with experimental data (open symbols) from Mileo et al.64 of methane (303 K), ethane (295 K), propane (323 K) and butane (298 K) in Cu-BTC, (b) GCMC-simulated CO2 single-component isotherm at 298 K in Cu-BTC compared with experimental data from NIST/ARPA-E database.66 Full details of the references for the 16 experiments are listed in the Supporting Information. (c) GCMC-simulated 8-component


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mixture (bulk phase composition 95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.001% TBM and 0.7% CO2) absolute loadings compared with IAST predictions in Cu-BTC from 5.8 bar to 65 bar at 298 K.

Figure 2. (a) Adsorbed phase mole fraction at the end of each cycle for a five-component mixture in CuBTC at 298 K with a bulk gas inlet flow of 95.11% methane, 3.5% ethane, 0.6% propene, 0.09% butane and 0.7% CO2, and (b) the deliverable capacity of each component. Results are from simulations of 100 cycles, but, for clarity, not every cycle is shown.

To illustrate the physical behavior during cycling, a simpler five-component mixture (bulk phase composition 95.11% methane, 3.5% ethane, 0.6% propene, 0.09% butane and 0.7% CO2) was simulated in Cu-BTC for 100 cycles. Figure 2(a) shows the mole fraction of components in the adsorbed phase measured at the end of each cycle (P = 5.8 bar), where the fraction of adsorbed methane decreases with



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the cyclic system, and a similar tendency in deliverable capacity is observed in Figure 2(b). The results in Figure 2 demonstrate that propane and butane gradually accumulate in the adsorbent, while CO2 and ethane accumulate during the first 10 cycles then decrease slowly due to their weaker adsorption affinity than the former components. It is notable that the system has not fully converged to a cyclic steady state after 100 cycles. This is the generic behavior expected in a cyclic adsorption system,14, 27, 34-35, 67 that is associated with the stronger adsorption affinity of the heavy components (as evidenced by the heat of adsorption shown in Figure S4).35 To further quantify the tank’s performance, the molar ratio of each component between the outlet and inlet flow is defined as: =

STRU

(11)

Here the mole fraction of the gas phase in tank equals the mole fraction in the outlet flow. Since the inlet molar fraction is constant, the molar ratio reflects the percentage of component i delivered to the engine during the release process. As an example, Figure 3(a) shows the molar ratio of propane as a function of pressure during the release at 1st, 50th and 100th cycle under the same conditions shown in Figure 2. The other four components are illustrated in Figure S7. In the 1st cycle, almost no propane is observed in the outlet flow, indicating that essentially all of the propane introduced in the tank in this cycle accumulates in the tank. To illustrate the performance of the tank over 100 cycles, the average ratio of each component during each cycle is shown in Figure 3(b). It is notable that considerable accumulation of butane in the tank is still occurring after 100 cycles, the average ratio of ~0.6 for butane during the 100th cycle indicates that ~40% of butane introduced into the tank in that cycle accumulates rather than being released during desorption.


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Figure 3. (a) Mole fraction ratio of propane between outlet and inlet flow during 1st, 50th and 100th cycle under the same conditions shown in Figure 2 and (b) the average mole fraction ratio of each component between outlet and inlet flow in Cu-BTC at 298 K.

As mentioned above, care must be taken in numerically solving the equations that describe the tank model when components with strongly varying adsorption affinities are present. We used an approach 17 ACS Paragon Plus Environment


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based on rescaling factors from a trust-region algorithm, as described in the Supplementary Information. To validate this approach, calculations with the five-component mixture just described were performed with and without the rescaling factor approach, since a direct calculation was feasible for this relatively simple mixture. The results in Figure S8 show the very close agreement between these two approaches. When the mixture was extended to eight components, it was not possible to obtain well converged numerical results without using the rescaling approach. We therefore employed rescaling factors in simulating the adsorption of the eight-component mixture (95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.001% TBM and 0.7% CO2) over 100 cycles in CuBTC, as shown in Figure 4. Comparing with the results in Figure 2, it can be seen that the additional components (pentane, hexane and TBM) further reduce the amount of methane adsorbed in the tank and also slow the rate at which the tank approaches a cyclic steady state. A secondary effect of the additional components (pentane, hexane and TBM) is that they shorten the total time associated with each cycle of the tank, as illustrated in Figure S9. The time required for 100 cycles is about 550 hours for the fivecomponent mixture, and this number drops by approximately 30 hours for the eight-component mixture. This change is a consequence of the constant mass flow rate we assumed in the outlet (see Eq. 8). This assumption leads to an almost constant molar flow rate Nf (molecules/uc/s), but the number of delivered molecules decreased considerably when introducing more components, which results in less working time. Perhaps more importantly, the additional components are also detrimental to the deliverable energy of the tank, as shown in Figure S10, where the eight-component mixture provides 11.2% less energy than that of five-component mixture at the 100th cycle.


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Figure 4. (a) Adsorbed phase mole fraction at the end of each cycle for an eight-component mixture in Cu-BTC at 298 K with a bulk gas inlet flow of 95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.001% TBM and 0.7% CO2, and (b) the deliverable capacity of each component. Results are from simulations of 100 cycles, but, for clarity, not every cycle is shown.

3.2 Performance of tank with different MOFs In addition to Cu-BTC, the cycling properties of the eight-component mixture were modeled in another six MOFs (MOF-5, IGOCOX, VACFOV, ZIF-8, Zn-MOF-74 and ZEKRIS). These MOFs were chosen based on the binary selectivity of TBM over methane reported from a large library of MOFs by Nazarian et al.,18 among which ZEKRIS and VACFOV have the highest (6.895×105) and lowest (3.484×103) selectivity, respectively, while the other MOFs range between the two extremes. As shown in Table S3, ZEKRIS exhibits the smallest value in both largest cavity diameter68 (LCD, 5.65 Å) and



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pore volume (0.4919 cm3/g) among these seven MOFs. This, in turn, provides the strongest interaction environment in the pores for the adsorption of gas molecules, as evidenced by the heat of adsorption at infinite dilution shown in Figure S4. MOF-5, in contrast, has the largest cavities and weakest heats of adsorption among the MOFs we considered. We therefore focus primarily on exploring ZEKRIS and MOF-5 in the ANG system through 200 cycles. The composition of components during cycling in MOF-5 and ZEKRIS are demonstrated in Figure 5, and the deliverable capacity for the two MOFs are shown in Figure S11. MOF-5 shows similar accumulation behavior of impurities to Cu-BTC (Figure 4). In the case of ZEKRIS, CO2 is significantly enriched in the adsorbed phase rather than the heavier hydrocarbons (butane, pentane and hexane) and TBM, resulting from the smaller size and higher molar fraction of CO2 in gas phase than the latter components, which allows CO2 to accommodate in the limited pore space of ZEKRIS. Figure 6 shows the average mole fraction ratios during cycling of these two MOFs. Pentane, hexane and TBM are nearly undetectable in the outlet through 200 operating cycles for ZEKRIS, indicating the continued accumulation of these components over time.


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Figure 5. Adsorbed phase mole fraction at the end of each cycle for an eight-component mixture at 298 K with a bulk gas inlet flow of 95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.001% TBM and 0.7% CO2 in (a) MOF-5 and (b) ZEKRIS. Results are from simulations of 200 cycles, but, for clarity, not every cycle is shown.

Figure 6. The average mole fraction ratio of each component between the outlet and inlet flow at 298 K in (a) MOF-5 and (b) ZEKRIS. Results are from simulations of 200 cycles, but, for clarity, not every cycle is shown.

The cycling performance of the seven MOFs we considered is summarized in Figure 7. As shown in Figure 7(a), the mole fraction of methane stored in tank at 65 bar decreases monotonically with cycling in every MOF. MOF-5 has the highest methane fraction after 200 cycles among the materials we examined, but this fraction is less than 70%. In vehicular applications, we care more about the deliverable energy than the delivered methane purity. The total delivered energy efficiency during cycling is summarized for each material in Figure 7(b), where the deliverable energy is normalized by 21 ACS Paragon Plus Environment


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the deliverable energy from the first cycle, as defined in Eq. 13. The absolute deliverable energy from 1st and 200th cycles are provided in Figure S13 and Table S5. ∆c KBKTR Z

\]^_`]]a ]]b ]

_$_]$ = ∆cKBKTR d

(13)

After 200 cycles, the deliverable energy per cycle is reduced by between 25% (for MOF-5) and 53% (for VACFOV). It is useful to note that the results in Figure 7 are nonlinear with respect to the number of cycles, so care would need to be taken in extrapolating results of this kind from a limited number of cycles. Figure S12(a) illustrates the deliverable capacity of each component in the first cycle. MOF-5 and ZEKRIS provide the largest and smallest delivered amount of methane, respectively, among the MOFs studied. Because of the combined effects of MOF pore size and adsorption affinity, the deliverable capacity of methane decreases at different rates in various materials. If we define the delivery efficiency of methane as the normalization of the deliverable capacity of methane with its value at the first cycle, the delivery efficiency of methane after the 200th cycle follows the order MOF-5 > Cu-BTC > ZEKRIS > ZIF-8 > Zn-MOF-74 > IGOCOX > VACFOV (see Figure S12(b)). That is, MOF-5 shows the most stable methane amount through 200 operating cycles. The delivery efficiency of methane correlates strongly with the overall deliverable energy because in every example methane is the dominant component of the mixture delivered by the tank.


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Figure 7. Tank performance at 298 K for storage of eight-component mixture with a bulk gas inlet flow of 95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.001% TBM and 0.7% CO2: (a) Mole fraction of methane stored in tank at 65 bar, and (b) normalized deliverable energy relative to the deliverable energy from the first cycle. Results are from simulations of 200 cycles, but, for clarity, not every cycle is shown.

The selectivity of methane, hexane and TBM over the other components in tank at the end of the 1st and 200th cycles is shown in Figure 8. The methane selectivities in the figure have been multiplied by 1×104 so the data for each component is on a similar scale in the figure; the methane selectivities are lower than one and are several orders of magnitude lower than for the heavier components, although methane is the dominant component in the mixture because methane has the weakest adsorption affinity of any of the species we considered (see Figure S4). The slightly lower methane selectivity in ZEKRIS than other MOFs results from the small pore size of ZEKRIS reaching saturation upon 5.8 bar of pressure, as evidenced by the single component isotherm shown in Figure S1(g). In contrast, the high 23 ACS Paragon Plus Environment


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adsorption affinity of hexane and TBM gives rise to their high selectivities in the tank. As illustrated in Figure 5 and Figure 6, the mole fraction of methane in MOF-5 and ZEKRIS decreases in both adsorbed and gas phases, while the mole fractions of hexane and TBM increase in both phases. The selectivity reduction at the 200th cycle indicates that the composition of methane in adsorbed phase decreases more strongly than that in gas phase, while the fraction increments of hexane and TBM in the gas phase are greater than those in the adsorbed phase.

Figure 8. Selectivities of methane, hexane and TBM in the tank at the end of the 1st and 200th cycles relative to the eight-component bulk mixture of 95% methane, 3.5% ethane, 0.6% propane, 0.09% butane, 0.064% pentane, 0.045% hexane, 0.001% TBM and 0.7% CO2. The selectivities of methane were multiplied by 104 for scaling purposes.


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Summary In this work, an isothermal model was developed by combining GCMC simulations, IAST and mass conservation to describe a MOF-based ANG system for storage of pipeline natural gas during long term cycling. Seven MOFs with various structural properties were considered as adsorbents, while the adsorption/desorption behaviors of the pipeline gas mixture with up to eight components was studied to investigate how the impurities admixed in pipeline gas affect the performance of ANG systems. Use of a rescaling method made it possible to overcome the numerical challenges associated with adsorbing components with greatly differing adsorption affinities. As expected, the gradual accumulation of heavier components in the tank reduces the amount of methane and total delivered energy from the tank during cycling. None of the examples we studied completely reached a cyclic steady state after 200 cycles. Calculations of the kind are useful for understanding the influence of physical properties such as the heats of adsorption for each component on the levels of accumulation in the tank upon cycling. In each case, methane was the dominant component in the delivered gas, both in terms of mole fraction and deliverable energy. Nevertheless, our results point to the importance of quantifying the long term impact of natural gas impurities in considering ANG as an avenue for methane storage. There are various approaches that could be used to mitigate the negative effects of impurity accumulation in an ANG system for methane. Our calculations assumed that cycling was performed at a uniform temperature and with fixed adsorption and desorption pressures. Periodic application of a cycle that led to stronger desorption, either by elevating temperature or desorbing at lower pressure, would reduce although not eliminate the “capacity fade” associated with impurity accumulation. The methods we have used here would be useful to explore the relative merits of these approaches. It is important to note that our models assume the adsorption of every component is completely reversible in the sense that the adsorbates do not affect the structure of the adsorbents in any way. If 25 ACS Paragon Plus Environment


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adsorption of one or more components led to structural change or degradation of the adsorbent, this would likely lead to irreversible decrease in the capacity of an ANG tank. Reports exist where extended exposure to even trace levels of some impurities can induce significant degradation of some MOFs.32, 6971

Unfortunately, little is currently known about the mechanisms associated with this kind of degradation

for the specific impurities that might be present in pipeline natural gas. Developing experimental methods to efficiently probe these issues would contribute positively to the development of ANG adsorbents that are well suited for practical applications.

ASSOCIATED CONTENT Supporting Information GCMC-simulated single component isotherms, Langmuir fitting parameters, the state parameters, heat of combustion of eight adsorbing components; physical properties of seven MOFs; details of model solving technique; heat of adsorption at infinite dilution; mole fraction ratio between outlet and inlet flow for methane, ethane, butane and CO2; adsorbed phase mole fraction of five-component mixture with and without rescaling factor approach; pressure profiles, deliverable energy of five-component and eight-component mixtures; and deliverable capacity, deliverable energy of MOFs. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *[email protected]


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Notes These authors declare no competing financial interest.

ACKNOWLEDGEMENTS DT and DSS acknowledge support from the Department of Energy Nanoporous Materials Genome Center, supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Chemical Sciences, Geosciences and Biosciences under Award DEFG02-12ER16362. RJV and DSS acknowledge support from the National Science Foundation grant number 1604375. We thank Jongwoo Park for assistance in compiling experimental data for CO2 adsorption in Cu-BTC. YW thanks the China Scholarship Council (CSC) (No. 201506150058) for a fellowship to visit the Georgia Institute of Technology. Helpful conversations with Prof. Krista Walton were greatly appreciated.

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Impacts of Gas Impurities from Pipeline Natural Gas on Methane

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