Pressure Swing Adsorption for Biogas Upgrading with Carbon

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Pressure swing adsorption for biogas upgrading with carbon molecular sieve Rafael Luan Sehn Canevesi, Kari Anne Andreassen, Edson Antonio da Silva, Carlos Eduardo Borba, and Carlos A. Grande Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b00996 • Publication Date (Web): 16 May 2018 Downloaded from http://pubs.acs.org on May 17, 2018

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Industrial & Engineering Chemistry Research

Pressure swing adsorption for biogas upgrading with carbon molecular sieve Rafael L. S. Canevesi 1,2, Kari A. Andreassen1, Edson A. da Silva2, Carlos E. Borba2, Carlos A. Grande1,* 1

SINTEF Industry, Forskningsveien 1, 0373 Oslo, Norway Western Paraná State University, Chemical Engineering, Faculty Street 645, La Salle Garden, Toledo, PR, Brazil 2

ABSTRACT: This work focuses on the study of a pressure swing adsorption (PSA) process for biogas upgrading using a carbon molecular sieve (CMS) adsorbent. Adsorption equilibrium and diffusion data of pure components was used to predict the multicomponent behavior. To validate the prediction of multicomponent adsorption at different concentrations, breakthrough curve experiments were performed for a gas mixture at different pressures (0.25, 0.5, 1 and 5 bar). Based on basic information, a model was used to predict the performance of a 2-column PSA unit. The mixture used as feed was 60% CH4 and 40% CO2 and pressure swings between 5 bar in adsorption mode to 0.1 bar in blowdown. Experimental data demonstrated that the model could describe the PSA performance with good accuracy. We have evaluated the influence of different feed times in the bio-methane recovery and purity. Bio-methane purity higher than 97.5% with recovery higher than 90% was obtained.

KEYWORDS: bio-methane upgrading; carbon molecular sieve; mathematical modeling; breakthrough curves, pressure swing adsorption.

*

E-mail address: [email protected]

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Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

INTRODUCTION

Climate changes caused by global warming are one of the biggest concerns of mankind due to their impacts on fauna, flora and of microorganism of world1–3. The most abundant anthropogenic greenhouse gases are carbon dioxide, methane and nitrogen oxide4,5. Moreover, the greenhouse warming potential of CH4 is 28 times higher than CO26. The global efforts to reduce the greenhouse gas emissions and at the same time satisfy the growing demand for fuels and energy, demands the deployment of new sources of alternative energy7. Biogas is produced from anaerobic digestion in fermentation of organic matter and is mainly composed by methane8. If biogas is collected to prevent its emissions to atmosphere, it can also be used as a renewable and sustainable source of energy or fuels9. Since biogas has only approximately 40-70% of methane10, it can only be employed as a source of power or heat in areas next to the generation source11. For utilization as a fuel or for transportation using the existing pipelines for natural gas, biogas has to be upgraded. The main contaminant of biogas is CO2 (30-60%) which means that upgrading will be a bulk separation of this gas12. Contrary to natural gas, the amount of C2+ in biogas is rather small and a pre-treatment for condensable gases is generally not used (in some places, propane is even added before grid injection). Benzene and toluene together have around 5 mg m-3 13. Indeed, there are many technologies to upgrade biogas to bio-methane to be injected in the existing infrastructure for natural gas transport. For this purpose, the obtained bio-methane has to satisfy the pipeline-grade specifications associated to each country14. The most common techniques are membranes process15,16, absorption17–19, cryogenic distillation20 and adsorption21– 23

. Adsorption process as like pressure swing adsorption (PSA) have a great potential for


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biomethane purification23. A short description of advantages and disadvantages of the different techniques is listed in Table 1 8,24,25. When a PSA unit is designed, a critical choice is the adsorbent that will be used. The adsorbent must have high selectivity, service life and easy regeneration26. Some adsorbents have proved to be efficient for upgrading biogas. ETS-4 modified with alkali-earth metals is also commercial for this application27,28. Zeolites were also used29,30. Lately, metal-organic frameworks were tested for this application with some good results31–33 One possible way to induce selectivity in the adsorbent is to tune the micropore diameter so that carbon dioxide can penetrate the pores while limiting methane diffusion through them34. A good example of such a kinetic adsorbent is carbon molecular sieve (CMS). The CMS adsorbents are made by partially constraining the pore mouth of carbonaceous materials to a given diameter that is used as limit to diffusion of molecules through the pores35. Most of CMS adsorbents available in the market do not have a significant difference in the equilibrium adsorption capacity of methane and carbon dioxide. But the pore constriction makes, the adsorption rates of these two gases are substantially different35. When using CMS materials, adsorption of water follows a Type V isotherm36. To avoid a reduction of CO2 selectivity, water has to be previously removed. This can be done in a pre-treatment column or in the same bed37,38. To maximize the adsorption properties of a given material, the right process design has to be implemented. There are several operating parameters like pressure levels, flowrates and times of the different steps that should be evaluated and optimized to obtain a good performance. To reduce the number of required experimental data to evaluate the effect of several operating variables, mathematical modeling can be a very valuable tool39,40.



In this work, we have evaluated the use of CMS KP 407 in a PSA process to upgrade biogas to pipeline-quality bio-methane. We have employed existing single gas data of adsorption equilibrium and kinetics41 to obtain parameters that were used to predict multicomponent adsorption and to design a PSA cycle with 6 steps (pressurization, feed, high and low-pressure blowdown, purge and equalization). The ability to predict multicomponent behavior at different conditions was tested by measuring experimental data of CH4-CO2 mixture at different total pressures of 0.25, 0.50, 1.00 and 5.00 bar. The authors are not aware of other experimental data in open literature measuring breakthrough curves under vacuum. PSA experiments with two columns were conducted also to validate the mathematical modelling. MATERIALS AND METHODS The breakthrough and PSA experiments were conducted in a multi-column PSA unit. A simplified scheme used for the experiments is shown in Figure 1. The gases exiting the unit are analyzed by mass spectroscopy that is coupled with an injection of a tracer gas (argon) to determine the flow variations due to bulk adsorption and pressure changes. Two stainless-steel columns were packed with adsorbents to make breakthrough curves and PSA experiments. In both columns, the temperature changes were monitored by one multipoint thermocouple located in the center of the column with four measuring points at 0.10, 0.26, 0.40 and 0.56m from the feed inlet. The breakthrough curves were measured at 0.25, 0.50, 1.00 and 5.0 bar. All experiments were made in both columns in order to clock result reproducibility and also to understand if there are differences in the behavior of the columns that is not attributable to the weight of the adsorbent. The operating conditions of the experiments are showed in Table 2. Gases with high purity and without further treatment were used (all provided


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by Yara Norway): He with purity >99.996%, Ar with purity > 99.996%, CO2 with purity > 99.9992% and CH4 with purity > 99.9995%. The same columns used for the breakthrough experiments (Fig. 1) were employed for 2column pressure swing adsorption (PSA) experiments. The PSA cycle has six steps: pressurization, feed, depressurization with equalization, blowdown, purge and pressurization with equalization. Experimentally, we had to divide the blowdown step into two sub-steps in order to protect the membrane vacuum pump of getting a higher pressure. For this reason, in this work the blowdown step was divided into high-pressure blowdown and low pressure blowdown and for this reason the figures display seven steps. The purge step was performed at low pressure with part of the CH4-rich product corresponding to a purge/feed ratio of 0.04. One cycle schedule is shown in Figure 2.

MATHEMATICAL MODELLING

Adsorption Equilibrium of Pure Gases

The multi-site Langmuir (MSL) model42 was used to fit the adsorption equilibrium data. The main difference between this model and the classical Langmuir isotherm is that the MSL model accounts for the variation of adsorbate size; the adsorbate particles occupy a determined number of adsorbent sites (α). The MSL model is described by:

qeq qmax

α

 qeq   = K ⋅ P1 − q max  

(1)



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where K is the adsorption constant, qmax is the maximum adsorption capacity and α represents the number of sites from adsorbent that can be occupied by the adsorbate molecule. The dependence of the adsorption constant with temperature is described by an Arrhenius equation:

K = K 0e

−

∆H RT

(2)

where K0 is the adsorption constant at infinite temperature, R is the ideal gas constant, ∆H is the heat of adsorption and T is the temperature. This model can describe adsorption equilibrium of multicomponent mixtures based on parameters of adsorption equilibrium extracted from pure gas measurements. The multicomponent model is a theoretical extension of the MSL model given by:

qeq,i qmax,i

α

n q   = Ki ⋅ P ⋅ yi 1 − ∑ eq,i   i=1 qmax,i 

(3)

To satisfy the assumptions about the fixed number of sites, the product of the maximum adsorption capacity and the number of sites is a constant number43. The model parameters were obtained by minimizing the error of fitting the equations given above to the experimental data. The minimization routine was written in Python computational language, with Powell optimization method.

Adsorption Kinetics of Pure Gases The adsorbent extrudates can be considered as bi-disperse, with macropores and micropores. However, for the gases used in this study the controlling resistance is within the micropores. This resistance can be to diffuse within the micropores of the material or located in the micropore mouth by the barrier generated when CMS was prepared.


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To estimate the micropore diffusion kinetics of pure gas, the experimental data was fitted using the dual-resistance model with micropore diffusivity ( the mouth of the micropores (

) and surface barrier resistances in

). To avoid the effect of the non-linearity of the isotherm and to

assume isothermal conditions, only the first point of each isotherm at low partial pressures were used. In a batch adsorption experiment, the molar balance between time 0 and any given time t can be described by the following equation: (4) where C(t) and C0 are the gas concentration at time t and 0 respectively, ms is the adsorbent mass, V is the adsorber volume and

is the average amount adsorbed in the extrudates, which

can be described with the following equation: (5) where Rp is the particle radius, and

is the averaged amount adsorbed in the macropores. The

mass balance in the adsorbent particle can be described by: (6) where Dp is the pore diffusivity, ρp and εp are the density, Cp is the micropore gas concentration and the porosity of the particle respectively. The averaged amount adsorbed in the micropores is defined by: (7) where rµ is the radius of the microparticle. The Fickian description of diffusion in a microparticle is:



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(8) where Dµ is the micropore diffusivity. These equations system are solved with the following initial conditions: (9) (10) Also, the following boundary conditions are needed: (11)

(12) (13)

(14) where kf and kb are the external mass transfer resistance and the barrier mass transfer resistance in the mouth of the micropores, respectively and qeq,i is the adsorbed phase concentration in equilibrium with Cp(r, R). These two resistances can be combined to a lumped parameter model using a linear driving force simplification. The LDF constant was calculated using the following equation22: (15)

The mathematical model was solved using gPROMS (PSE Enterprise, UK). The third order orthogonal collocation on finite elements method with 15 elements was used to discretize the equations in both radial directions. The DAE system was solved with DASOLV method.


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Breakthrough in fixed-bed column and PSA

The mathematical model used to describe the binary adsorption behavior has the following assumptions: • Ideal gas behavior • Heat, mass, and momentum transport in the radial directions are neglected; • Ergun equation can be used to describe the momentum balance; • Use a bi-LDF model to simplify the macropore and micropore diffusion equations; • Consider the mass transfer resistance surrounding the pellets; • The void fraction, cross section area and adsorbent properties are the same in all column; • Energy balances to describe heat transfer in different phases (gas, solid and wall);

With these assumptions, the mass balance for each component in the gas phase is: (16) where Ci is the gas phase concentration, Dax is the axial dispersion coefficient u is the superficial velocity, yi is the molar fraction, kf,I is the film mass transfer resistance, Cpi is the averaged concentration in the macropores, Ct is the total gas concentrations, εc is the column void fraction, ap is the pellet specific area and Bi is the Biot number. The mass balance in the macropores is described by: (17)



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where Dp,i is the macropore diffusivity, rp is the pellet radius, εp is the pellet void fraction, ρp is the pellet density and qi is the averaged adsorbed concentration. The mass balance in the micropore is given by: ∂q i = K µ (q eq ,i − q i ) ∂t

(18)

where Kµ is the micropore diffusion. The Ergun equation is used to describe the pressure drop behavior along the column:

µ (1 − ε ) ρ (1 − ε ) ∂P = −150 G 3 2 C u − 1.75 G 3 C u ⋅ u ∂z εCd p εCd p 2

(19)

where P is the total pressure, µG is the gas viscosity, ρG is the mass density and dp is the pellet diameter. The energy balance in the gas phase is:

∂T ∂  ∂T  ∂(uT ) ∂C = λ + εC R ⋅T T  − CT CPg ∂t ∂z  ∂z  ∂z ∂t 2h − (1 − ε C )aP h f (T − TS ) − w (T − TW ) rW

ε C CT CVg

(20)

where T, Ts and TW are the temperature respectively in the gas phase, adsorbent and column wall, CPg and CVg as the gas molar specific heat at constant pressure and volume respectively, rW is the wall internal radius, R is ideal gas constant, λ as the heat axial dispersion coefficient and hW and hf as the film heat transfer coefficient between the gas phase and wall, and between gas phase and adsorbent respectively. The energy balance in the solid phase is:


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nc  nc ( ) (qiCV ,ads ,i ) + ρ PCPs  ∂TS = ε ρ + Cf C ∑ ∑ P i V i P ,  i =1  i=1  ∂t nc    ∂Cf i  ε ⋅ R T  + aP h f (T − TS ) S ∑  P i =1  ∂t   

+

(21)

ρ L nc  ∂q   − ∆H i i  ∑ (1 − ε C ) i=1  ∂t 

where CV,ads,i is the molar specific heat at constant volume of adsorbed, CPs as the mass heat capacity coefficient of the column. The energy balance of the column is given by: ρ W C Pw

∂ TW = α w hw (T − TW ) − α wL U (TW − T∞ ) ∂t

(22)

where CPw is the wall specific heat, U is the overall heat transfer coefficient, T∞ is the external temperature, ρw is the wall density and αw and αwl are respectively the ratio of the internal surface area to the volume of the column wall and the ratio of the logarithmic mean surface area of the column shell to the volume of the column wall, descripted by the following equations:

αw =

dW e(d w + e )

α wl =

1 d +e (d w + e )ln  w   dw 

(23)

(24)

where e as the thickness of the shell. The boundary conditions used to solve the system of partial differential equations for a PSA process are shown in Figure 3. The mathematical model was solver using gPROMS (PSE Enterprise, UK). The third order orthogonal collocation on finite elements method with 50 elements was used to discretize in the axial direction. The DAE system was solved with DASOLV method. This mathematical model was previously derived44 and has also been used for other PSA applications45,46.



RESULTS AND DISCUSSION

Adsorption equilibrium and kinetics of pure gases

The adsorption equilibrium of pure carbon dioxide and methane at two different temperatures (298 and 343K) obtained previously in our laboratory41 together a new fitting of the multisite Langmuir model are shown in Figure 4. The adsorbent is more selective to carbon dioxide despite the equilibrium selectivity is not very high. The adsorption equilibrium of pure gases in this kind of adsorbent was reported in previous works for carbon dioxide23,41,47–50 and methane41,51–54. Our experimental data is similar to previously reported data. All fitting parameters of the multisite Langmuir model for pure gases are shown in Table 3. The multisite Langmuir model could describe the singe gas equilibrium adsorption very well, and this model has the advantage of having an extension to predict multicomponent behavior using single gas data, which can be used in breakthrough and PSA modeling. The adsorption rate curves of both gases at 298 K are shown in Figure 5. Note that carbon dioxide diffusion rate is much faster than the diffusion rate of methane. The methane adsorption equilibrium is reached only after 24 hours of contact gas-solid, while carbon dioxide equilibrium was reached after around 15 min. This behavior was also reported in other CMS adsorbents for methane and carbon dioxide23,47,48. The difference can be attributed to the diameter size difference between molecules and micropore mouths23,41,55, or the electronic properties and molecular shape that can influence in the sorption kinetics47,56. The time in Figure 5 is displayed as square root in order to highlight the effect of the mass transfer resistance in the mouth of the micropores57,58.


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In this adsorbent, micropore resistance controls the diffusion of carbon dioxide, while the control of methane diffusion is shared between micropore resistance and the surface barrier at the mouth of the micropores. The fitting parameters obtained in this work to describe the diffusion of pure gases on CMS KP 407 are reported in Table 4.

Multicomponent breakthrough experiments

Breakthrough curves were measured at four different pressures. Examples at total pressures of 5.0 and 0.25 bar are displayed in Figures 6 and 7, respectively. Breakthrough curves at 0.5 and 1.0 bar are shown in the Supporting information. As energy and mass transfer are interlinked, we also show the temperature evolution in four different positions inside the column. The experiments were conducted under non-isothermal and non-adiabatic conditions. The shape of the all breakthrough curves presented are close to other previous works with CMS23,41. There are some differences between the predicted and the estimated temperature profiles, particularly at the top of the column. A possible reason for that is that temperatures were monitored with a multipoint thermocouple that is introducing a small, but apparently accountable, axial transport of heat through the column. The results show that methane is almost not adsorbed in the column, while carbon dioxide is preferentially retained by the adsorbent. The effect of the non-linearity of the CO2 isotherm is observed in the images: at lower pressure the steepness of the breakthrough curves is less pronounced (less compressive concentration waves). The effect of pressure is also reflected in the temperature increase due to adsorption: at lower pressures there is less gas adsorbed and



temperature increase is not higher than 6K but at higher pressures the temperature increase is around 30K. If the system was isolated, the thermal excursion would have been even higher. The results of the breakthrough curves at different pressures showed that the using a constant diffusion coefficient for CO2 and CH4 does not result in significant deviations. Using the Darken’s rule of dependence of the diffusion with the loading should be used in case that higher pressures are used at the expense of computational efforts28,59. These results confirm the high dynamic selectivity of the CMS KP-407 towards carbon dioxide. These experiments also allowed us to verify the prediction of the multicomponent adsorption and the mathematical model used for describing adsorption in a fixed-bed under different conditions. Although in the low-pressure steps of a PSA unit the column will be richer in CO2, having experiments at lower total pressures was important to understand the validity of the model in the whole pressure range that will be used for PSA operation. This means that the model should be able to predict the behavior of PSA unit for bio-methane recovery.

PSA experiments The PSA cycle was Skarstrom-type with one pressure equalization and purge with product. At the beginning of the experiments, the column was pressurized to 5.0 bar using helium. The feed composition was 60% methane and 40% carbon dioxide and total flow rate was 0.7 SLPM. The experiment was performed inside the oven at 305 K. PSA experimental profiles of mass and pressure with mathematical modeling prediction are shown in the Figure 8. Similar image for a feed time of 8 minutes is shown in the Supporting information. Since CMS is a kinetic adsorbent, cyclic steady state (CSS) is achieved after 100 cycles. The prediction of the temperature excursion in the different positions of the column is very good.


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In PSA mode, the observed differences between model and experimental data are smaller than when performing breakthrough. CSS is assumed when the variation of the different variables of the system do not pass a given threshold59. As shown in Figure 9, the thermal profiles inside the column seem to reach CSS after 15 cycles. Between cycles 15 and 100, methane is slowly displacing minor amount of CO2. Since the heat of adsorption of both gases is not very different, it is not possible to detect significant thermal changes or performance variation. Using the mathematical model is also possible to know what happens inside the column during each cycle. The simulated internal temperature evolution at the end of the different steps within a PSA cycle in CSS is shown in Figure 10. It is possible to see how the temperature increases during pressurization and feed steps, and decreases in the recovery steps (equalization, high pressure blowdown, low pressure blowdon, purge and equalization). Temperature oscillations in the PSA experiments are in the order of 30-40 K when CSS was reached. It is known that temperature oscillations affect negatively PSA performance60. Other techniques can be applied to reduce temperature variations61,62. Although these options render better cyclic adsorption capacities, they tend to use additional components and thus increase the cost of the upgrading. Figures 8-11 confirm that the mathematical model describes the experimental PSA experiments with good accuracy. For this reason, simulations with different feed times were performed, with the purpose of evaluating the response of the system to this operating variable. Also, a simulation with same feed times without the purge step were performed. The methane recovery and purity after the cycle steady state was reached are shown in Figure 11. The recovery of methane increases with longer feed times: 91.7 % and 93.8 % for feed time of 8 and 11 min, respectively. The typical trade-off between higher purity and lower



recovery is observed. However, bio-methane with pipeline quality (purity > 97.5%) was obtained in all cases. Also, results in Figure 11 indicate that including a purge step in the cycle results in a reduction in methane recovery which is very important since in some countries methane slip is limited. The results obtained here indicate that bio-methane with pipeline-quality can be obtained with this material at high recovery. However, to obey strict regulations of methane slip, more process measures should be performed or more columns should be used.

CONCLUSIONS

In this paper, the upgrading of a synthetic binary mixture of CO2-CH4 mixture was done using a pressure swing adsorption (PSA) process with carbon molecular sieve (CMS) KP-407 as selective adsorbent. The equilibrium data of pure gases was fitted with the multisite Langmuir model and successfully used to predict multicomponent data. The data of diffusion at low partial pressures was fitted to a dual resistance model composed by micropore diffusivity and a barrier resistance in micropore mouth. The analysis of the diffusion process demonstrated that pore mouth barrier was the dominant mechanism of resistance for methane, while the diffusion inside de pores was the main resistance for carbon dioxide. The CMS material presents a large kinetic selectivity to carbon dioxide observed in experimental breakthrough data measured at the feed pressure (5 bar), but also under vacuum conditions (0.25 bar). The breakthrough results allowed us to verify that the fixed-bed mathematical model can describe experimental data and was used for predicting the PSA behavior. The 2-column PSA process using this material is very effective: bio-methane with pipeline-quality (

Pressure Swing Adsorption for Biogas Upgrading with Carbon

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