CO Enrichment from Low-Concentration Syngas by a Layered-Bed

May 23, 2017 - Similarly, a layered-bed VPSA process with Cu(I)AC and Cu(I)Y layered .... Adsorption (AD): Feed syngas is transported continuously and...
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CO Enrichment from Low-Concentration Syngas by a Layered-Bed VPSA Process Yan Zhou, Yuanhui Shen, Qiang Fu, and Donghui Zhang* State Key Laboratory of Chemical Engineering, Collaborative Innovation Center of Chemical Science and Engineering, Research Center of Chemical Engineering, School of Chemical Engineering and Technology, Tianjin University, Tianjin 300350, China S Supporting Information *

ABSTRACT: A layered-bed vacuum pressure swing adsorption (VPSA) process with a hybrid packing of Cu(I)AC and Cu(I)Y adsorbents was developed to recover CO from a low-concentration syngas mixture (2.4%CH4−32.3%CO−1.0%CO2−46.0%H2− 18.3%N2). Prior to performing a sequential separation VPSA process design, the statical adsorption equilibrium isotherms of pure CH4, CO, CO2, H2, and N2 on two dissimilar improved copper-supported adsorbents of Cu(I)AC and Cu(I)Y were first determined under four different temperature values (293.15, 303.15, 313.15, and 323.15 K) with pressures up to 500 kPa. The experimental adsorption equilibrium data of the pure component were then proved to be well fitted by a Langmuir isotherm model. Further, multicomponent breakthrough curves were separately measured by experiments and simulations with a fixed-bed adsorption mathematical model to obtain a successful prediction for multicomponent adsorption dynamics and equilibrium. On the basis of these, a pilot-scale multibed VPSA simulation work with continuous feeding was performed to study the effects of operation conditions on the process separation performances so as to achieve optimization of process manipulation parameters. In the optimized layered-bed VPSA process design, results indicated that a high CO product purity of 99.05% with a recovery of 91.65% as well as an adsorbent productivity of 5.122 mol·kg−1·h−1 could be obtained under a relatively economic energy costing of 0.166 kW·h·Nm3− CO. purification technologies.5 There are three mainly feasible arts to accomplish this process known as cryogenic distillation,6,7 membrane screening,8,9 and adsorption-based splitting.10−12 However, comparatively speaking, from the perspective of industrialization, the pressure swing adsorption (PSA) process based on cyclic adsorption−desorption is more competitive in terms of high operating elasticity and process economics. Moreover, the key for obtaining a good separation performance by PSA is the selection of an excellent adsorbent to satisfy the requirement of a good technological deign. As for the CO adsorption separation procedure, a great challenge we are faced with is the similar adsorption properties of CO and N2 on commonly commercial adsorbents of activated carbon,

1. INTRODUCTION C1 chemistry, related to methane, methanol, carbon dioxide, carbon monoxide, etc., plays an irreplaceable role in the current supply of energies and chemicals compared to its global warming effect when discharged directly into the atmosphere.1 Concerns are emphasized on carbon monoxide; it is one of the most important chemical raw materials, normally applied to be converted into methanol, ethanol, ethylene glycol, acetic acid, and many other oxygenated species by CO hydrogenation.2 However, it generally exists in syngas mixtures produced from the steam methane reforming (SMR) process with various carbon resources such as coal, natural gas or shale gas, biomass, etc. 3 Apart from hydrogen of 32−67 mol %, other contaminants of carbon dioxide (1−28%), methane (0.1− 14%), and nitrogen (0.6−23%) are always associated with reducing the syngas grade as a result.4 A solution to upgrade the quality in improving utilization efficiency of syngas is enriching and separating CO from other impurities by economic © 2017 American Chemical Society

Received: Revised: Accepted: Published: 6741

January 17, 2017 May 22, 2017 May 23, 2017 May 23, 2017 DOI: 10.1021/acs.iecr.7b00229 Ind. Eng. Chem. Res. 2017, 56, 6741−6754

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Industrial & Engineering Chemistry Research molecular sieve, silica gel, etc.13 This limitation forces many researchers to seek for a finer CO adsorption performance, while it is practicable to modify the commercial adsorbents using copper to complexation with CO via π-bond which is stronger than the van der Waals interaction that occurred in typical adsorbent types.14−17 Two practical techniques aiming at designing Cu-based adsorbents with either carbon-supported or zeolite-supported for CO selective adsorption are available in industrial PSA application.14−17 In addition, recent research for the fabrication of cuprous active sites on metal−organic frameworks (MOFs) support with vapor induced reduction (VIR) method may have proved to be potential for CO adsorption application that deserved to be further developed.18−21 However, what is noteworthy is that π-complexation adsorption has a larger adsorption thermal effect, which will render a higher desorption coating. Besides, adsorption heat of CO on Cu-zeolite is much more than that of Cu-carbon;22,23 multifarious adsorbent packings in a layered adsorption fixedbed may be feasible in that case, but no research and applications were reported to be available. Nevertheless, the composited layered-bed with an activated carbon layer separating CO2 and CH4 and a zeolite layer purifying CO and N2 is the most important characteristic for industrial application in H2 PSA purification where we can take a case example to apply for the CO enrichment process.24−26 In the current study concerning our work, two kinds of copper modified adsorbents of Cu(I)AC and Cu(I)Y were prepared and employed in order to achieve a welled CO enrichment from an industrial syngas mixture (2.4%CH4− 32.3%CO−1.0%CO2−46.0%H2−18.3%N2).17 Pure component adsorption isotherms and multicomponent breakthrough curve measurements on these two adsorbents were first performed to evaluate the adsorption equilibrium and adsorption dynamics by experiments and simulations, repectively. Further, for pilotscale multibed VPSA process considerations, numerical calculations were then performed to assess the process separation performances. Simulations employing a layered five-bed seven-step VPSA cycle were carried out at different operating conditions such as layer ratio of Cu(I)AC to Cu(I)Y, and flow rates of feed, replacement and evacuation, suggested to gain a sequence setting of optimized manipulation parameters qualified for maximizing recovery, and productivity as well as minimizing the energy consumption under a specific purity demand. Finally, physical distribution performances of gas phase concentration, solid phase concentration, and temperature under optimized operating conditions were evaluated to quantificationally explain the separation and purification characteristics of the syngas layered-bed VPSA process.

different copper-supported adsorbents for Cu(I)AC14 with copper chloride impregnating and carbon monoxide reduction treatments in sequence and for Cu(I)Y17 obtained from a similar treating process as activated carbon while with a copper ion-exchange method instead of impregnating. Prior to applying these two modified adsorbents, effective adsorbent evaluation procedures were indispensable. In our work, BET measuring for the adsorbents was first carried out by nitrogen adsorption at 77 K using an automatic sorptometer BELSORPmax (MicrotracBEL Japan, Inc.). The measured and supplied adsorbent physical properties are detailed in Table 1. Before Table 1. Adsorbent Physical Properties of Cu(I)AC and Cu(I)Y property

Cu(I)AC

Cu(I)Y

shape type particle size (mm) average pore diameter (nm) BET surface (m2·g−1) particle porosity bed porosity particle density (kg·m−3) heat capacity (J·kg−1·K−1)

Cylindrical 2.0−4.75 1.76 1408.6 0.337 0.352 876 0.28

Sphere 1.7−2.0 3.57 675.8 0.350 0.450 1915 0.85

each experimental procedure run, both adsorbent types were degassed and heated for regeneration at 420 and 620 K under helium purging atmosphere, respectively. Besides, the chemical agents used as adsorbates were high purity CH4 (>99.999%), CO (≥99.9%), CO2 (>99.999%), H2 (>99.9999%), and N2 (>99.9999%). 2.2. Adsorption Equilibrium Analysis. Normally, adsorption interactions between adsorbate and adsorbent can be quantitatively determinated by a static adsorption equilibrium measurement to predict the adsorption equilibrium behaviors. As for our study, a volumetric method with an experimental apparatus similar to that reported by Nam et al.27 was adopted to determine the adsorption equilibrium of the pure component of CH4, CO, CO2, H2, and N2 at a temperature− pressure scale of 293−323 K up to 500 kPa. The graphical apparatus system for measuring adsorption equilibrium isotherms was presented in Figure S1. The system can be divided into three parts, namely feeding, adsorbing, and regenerating. The feeding part was arranged for introducing different pure gases stored in pressure cylinders. The adsorbing part consisted of a loading cell and an adsorption cell (stainless steel, 4 cm I.D.) which were connected to each other with a metering valve and were all placed in an ultrathermostat with a water bath, while the latter regenerating was achieved by a vacuum pump to exclude the impurities in the measuring system. Applying the volumetric method, a principle of calculating adsorption capacity was based on the changes of mole numbers of free gas in the two cells before and after adsorption occurring, namely measuring the pressure− volume−temperature (P-V-T). The volumes of adsorption system consisted of the loading cell volume of 100 cm3 and the adsorption cell of 50 cm3 including 10 g of each adsorbent packing as well as the dead volume, which were all determined by a method of helium expansion. Prior to each operation run, the adsorbents were all treated by way of thermal and vacuum regeneration before packing, and the adsorption measuring system was also evacuated by a vacuum pump to eliminate trace impurities. When the adsorption measuring system ran, each

2. EXPERIMENTAL SECTION 2.1. Adsorbent Materials and Chemicals. Adsorbent material is the key to applying the adsorption separation technology. Its two considerable aspects are selectivity and reversibility, and the former directly determines the separation factor of adsorbent, while the latter essentially gives an index to the regeneration costing of adsorbent. In order to achieve an extreme enrichment for CO from the syngas mixture, two kinds of original adsorbent materials of activated carbon (4−10 mesh, Cylindrical, Tangshan Tianhe Activated Carbon Co.) and NaY molecular sieve (10−12 mesh, Sphere, Shanghai Jiuzhou Chemical Co.) were employed in our work. The two original adsorbents were further separately applied to prepare two 6742

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Figure 1. Adsorption isotherms of pure CH4, CO, CO2, H2, and N2 on (a) Cu(I)AC and (b) Cu(I)Y.

pure adsorbate gas was first introduced into the loading cell until a stabilized pressure−temperature level was achieved, and then the balanced gas in the loading cell was allowed to flow into the adsorption cell for the adsorption procedure. The criterion to determine the acquisition of adsorption equilibrium state was that the temperature and pressure for both two cells kept constant from a data logger. Of course, we can obtain a series of adsorption equilibrium data for pure component of syngas that is widely used to portray as adsorption isotherms. In this study, the experimental adsorption equilibrium data was fitted by the Langmuir adsorption isotherm model represented in the following form of eqs 1−3. Figure 1 presents the experimental and fitted adsorption isotherms for pure five syngas components on these two adsorbents, respectively. The relevant Langmuir model fitting parameters are displayed in Table 2. qi* =

selectivity for applying either Cu(I)AC or Cu(I)Y to achieve perfect CO separation performance from the pentabasic syngas mixture. Nevertheless, a precipitous adsorption isotherm pattern of CO on Cu(I)Y compared to Cu(I)AC means poor desorption capacity, which will be inevitable to directly lead to an increase in the regeneration power consumption concerning a practical installation application. Therefore, a layered-bed PSA is more favorable, usually seen on industrial H2 purification PSA unit,28−30 which utilizes lower activated carbon packing as the CO2 separator and upper molecular sieve packing as the N2 purifier. Similarly, a layered-bed VPSA process with Cu(I)AC and Cu(I)Y layered packing simultaneously was also preferably considered in our work for CO enrichment study. Prior to carry out the layered VPSA process application, breakthrough curve experiments were performed to evaluate the multicomponent adsorption equilibrium and dynamics as well as provide a validation of the accuracy and reliability for modeling computations in the subsequent process simulation work. 2.3. Fixed-Bed Breakthrough Measurement. Experimental breakthrough curve measuring was implemented in a single fixed-bed adsorption apparatus detailed elsewhere in the literature.31−33 In this study, the graphical experimental installation system for multicomponent breakthrough measurement was presented in Figure S2, where the adsorption bed was a single stainless steel fixed-bed with a height of 1.0 m, an inner diameter of 0.25 m, a wall thickness of 0.01 m, and wall density of 7800 kg·m−3. Fixed-bed breakthrough experiments were conducted in three separated aspects: (i) single Cu(I)AC packing, (ii) single Cu(I)Y packing, and (iii) layered hybrid adsorbent packing. The latter layered-bed was packed with activated carbon (AC) in the bed bottom, and molecular sieve (MS) was packed on the top of AC layer after installing a metal screen of 0.25 mm thickness between the two layers, where the layer ratio was fixed to 7:3 (AC:MS). Besides, all of the backfilled adsorbents had undergone a deep degas disposing in a muffle (Central Furnace SK-G10123K type, China) device at a fixed vacuum purge pressure of 52 kPa by helium in advance. When each breakthrough experiment was run, a constant feeding condition with a 20% inert helium diluting composition of 20%He/1.92%CH 4 /25.84%CO/0.80%CO 2 /36.80%H 2 / 14.64%N2 at a total flow rate of 100 SLPM (stard liter per minute) under 293.15 K and pressure variations of 100, 250, and 500 kPa was taken into account in our work. The flow rate was controlled by MFCs (mass flow controllers) and measured by MFMs (mass flow meters), while the pressures were regulated by BPRs (back pressure regulators). Moreover, outlet concentration compositions vs time were serially analyzed with

qm , ibiPi 1 + biPi

(1)

bi = b0i exp( −ΔHi /R gT )

(2)

⎛ ∂ ln Pi ⎞ ⎟ −ΔHi = R gT ⎜ ⎝ ∂T ⎠q

(3)

As expected, significant differences can be found in experimental adsorption isotherms data of CO vs the other four components on both Cu(I)AC and Cu(I)Y. The CO has the highest competitive adsorption capacity either on the Cu(I)AC or Cu(I)Y, especially for the latter, and followed by CO2, CH4, N2, and H2. It reflects that high adsorption Table 2. Adsorption Isotherm Parameters Fitted by the Langmuir Equation adsorbent

adsorbate

qm (mol·kg−1)

Cu(I)AC

CH4 CO CO2 H2 N2 CH4 CO CO2 H2 N2

2.515 4.328 3.112 5.619 19.731 1.798 × 102 2.693 1.986 1.799 × 102 1.799 × 102

Cu(I)Y

b0 (kPa−1) 5.117 2.456 1.265 1.359 7.614 6.265 3.019 2.134 4.626 1.784

× × × × × × × × × ×

10−7 10−9 10−7 10−7 10−8 10−9 10−11 10−6 10−9 10−8

ΔH (kJ·mol−1) −17.29 −35.53 −21.72 −11.84 −15.66 −18.50 −56.08 −23.17 −16.50 −14.90 6743

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Industrial & Engineering Chemistry Research a fixed 120 s sample interval by a gas chromatograph (BeifenRuili SP2100A type, China) with a thermal conductivity detector (TCD). All acquired data was monitored and saved on the computer by an AD converter. Finally, all measured experimental breakthrough curve data were employed to compare with the latter simulation results to validate the accuracy and reliability of the numerical modeling.

visually describe the physical dynamic mass transfer behaviors between adsorbate and adsorbent considering a nonisothermal and nonadiabatic model based on the following assumptions:34,35 (a) Ideal gas with no compressibility effect utilizes. (b) Adsorption rate complies with linear driving force (LDF) model on a lumped resistance assumption and flow pattern satisfies the plug flow model with an axial dispersion term. (c) Thermal equilibrium reaches between gas−solid−wall phases with nonisothermal heat transfer resistances. (d) Temperature, pressure, and velocity gradients in radial adsorbent particle can be considered to be uniform. (e) Extended Langmuir adsorption isotherm pattern is applied to characterize the adsorption equilibrium behavior of the adsorbate onto adsorbent. The quantized equations concerning an adsorption fixed-bed model36,37 are presented in Table 3, and the initial and boundary conditions37 for the breakthrough and cycle operation are given in Table S1. Besides, the physical properties of the adsorbent bed are the same as the breakthrough experiment, and the involved transport model parameters are provided in Table 4 to achieve simulation runs.

3. COMPUTATIONAL 3.1. Mathematical Model. A practical mathematical model of adsorption fixed-bed, as shown in Figure 2, was developed to

Figure 2. Graphical depiction of the adsorption bed model.

Table 3. Model Equations of Adsorption Bed model equation

mathematical expression

− εbDax, i

∂ 2ci ∂z 2

+

∂(vgci) ∂z

(4a)

⎛ ε D i⎞ ⎟ εb⎜1 + 9.49 2bv m, ⎝ grp ⎠

(4b)

gas phase

− kg

∂ 2Tg ∂z 2 +

energy balance

∂q ∂ci + ρs (1 − εb) i = 0 ∂t ∂t

vgrp

Dax, i = 0.73Dm, i + mass balance

+ (εb + (1 − εb)εp)

+ Cvgvgρg

∂Tg

+ εbCvgρg

∂z

∂Tg ∂t

+P

∂vg ∂z

+ hf (Tg − Ts)

4hw (Tg − T0) = 0 Db

(5a)

solid phase

− ks

∂ 2Ts ∂z

2

+ Cpsρs

n n ⎛ ∂q ⎞ ∂Ts ∂T + ρs ∑ (Cpa, iqi) s + ρs ∑ ⎜ΔHi i ⎟ − hf (Tg − Ts) t ∂t ∂ ∂t ⎠ ⎝ i=1 i=1

(5b)

=0 bed wall 4D b ∂ 2Tw ∂T + Cpwρw w − hw (Tg − Tw) + hamb ∂z ∂z 2 (D b + Wt)2 − D b2

kw

4(D b + Wt)2 (D b + Wt)2 − D b2

(Tw − Tamb)

(5c)

=0 (1 − εb)ρg

2

150μ(1 − εb) ∂P = + 1.75M |vg|vg ∂z εb3(2rpψ )2 2rpψεb3

momentum balance

−

adsorption balance

qi* = ∂qi ∂t

adsorption rate

qm , ibiPi n

1 + ∑i = 1 biPi

,

bi = b0i exp(−ΔHi /R gT ),

= kLDF, i(qi* − qi) =

⎛T ⎞ Dk, i = 97.0rp ⎜ ⎟ ⎝ Mi ⎠ 6744

15De, i rp2

(qi* − qi),

De, i =

(6) ⎛ ∂lnPi ⎞ ⎟ −ΔHi = R gT ⎜ ⎝ ∂T ⎠q εp

Dk, iDm, i

τ Dk, i + Dm, i

(7a,7b,7c)

,

(8a,8b,8c) DOI: 10.1021/acs.iecr.7b00229 Ind. Eng. Chem. Res. 2017, 56, 6741−6754

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Industrial & Engineering Chemistry Research Table 4. Mass and Heat Transfer Parameters of Fixed-Bed Mathematical Model layered Cu(I)AC parameter −1

kLDF (s ) Dm × 105 (m2·s−1) Cps (kJ·kg−1·K−1) Cpw (kJ·kg−1·K−1) hf (W·m−2·K−1) hw (W·m−2·K−1) hamb (W·m−2·K−1) kg (W·m−1·K−1) ks (W·m−1·K−1) kw (W·m−1·K−1)

layered Cu(I)Y

CH4

CO

CO2

H2

N2

CH4

CO

CO2

H2

N2

0.028 6.44 0.796 0.504 169 65 60 0.247 0.28 17.0

0.174 2.75

0.105 0.94

0.693 8.21

0.193 3.57

0.069 2.48 0.950 0.504 155 65 60 0.247 0.85 17.0

0.053 1.41

0.092 0.90

0.119 7.36

0.077 1.31

Figure 3. Schematic diagram of the five-bed VPSA process in simulations.

Figure 4. Graphical cycle sequence introduced for VPSA process simulations.

3.2. VPSA Process Modeling of Multiple LayeredBeds. Experiment and simulation of single fixed-bed breakthrough curves are effective strategies to evaluate the adsorbent separation peculiarity and guide the process design but not applicable to an industrial large-scale separation unit because of

its poor automation and low product recovery. In order to enforce industrialized separation continuously under specific production demands, a multibed operation unit is widely accepted. In our work, a layered five-bed seven-step cyclic VPSA craft was preferred to make an all-sided performance 6745

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Industrial & Engineering Chemistry Research Table 5. Schedule Layout for the Five-Bed VPSA Process Employing the Proposed Cycle Sequence t/s bed bed bed bed bed

1 2 3 4 5

50

250

50

250

50

250

50

250

50

250

AD ↑ ER ↓ VU ↓ RP ↑ ED ↑

AD ↑ PR ↓ VU ↓ BD ↓ RP ↑

ED ↑ AD ↑ ER ↓ VU ↓ RP ↑

RP ↑ AD ↑ PR ↓ VU ↓ BD ↓

RP ↑ ED ↑ AD ↑ ER ↓ VU ↓

BD ↓ RP ↑ AD ↑ PR ↓ VU ↓

VU ↓ RP ↑ ED ↑ AD ↑ ER ↓

VU ↓ BD ↓ RP ↑ AD ↑ PR ↓

ER ↓ VU ↓ RP ↑ ED ↑ AD ↑

PR ↓ VU ↓ BD ↓ RP ↑ AD ↑

analysis for the high purity CO enrichment process by numerical simulation implementations. The designed VPSA process of schematic diagram is presented in Figure 3 which employed the cycle sequence in Figure 4, and the corresponding five-bed cyclic schedule arrangement with continuous feeding is also given in Table 5, where the ↑ represents a concurrent while ↓ denotes a countercurrent. As presented in Figures 3 and 4 and Table 5, a typical VPSA process consisted of the following seven steps: I. Adsorption (AD): Feed syngas is transported continuously and concurrently to flow through the adsorption layered-bed at a high pressure PH and controlled to terminate at the effective breakthrough state of CO. The heavy component CO is selectively adsorbed on the layered-adsorbents while the other four light contaminants are exhausted at the bed top. II. Equalization−depressurization (ED): The layered-bed with an effectively adsorbed equilibration is then depressurized concurrently from PH to a middling pressure level PM, and the released light components with high pressure are utilized to pressurize another bed undergoing ER step when step of VU is accomplished. III. Replacement (RP): It is also known as the heavy reflux (HR) step; the layered-bed is subsequently switched to a concurrent purge replacement step keeping a constant pressure level of PM. The sufficient replacement is achieved by adopting the heavy desorption gas produced from BD and VU steps and compressed from a normal pressure of PN. IV. Blow-down (BD): The layered-bed is then coming into a preliminary desorption stage to obtain a bulk high purity of CO gas in the form of countercurrent pressure dropping from PM to PN without power input. V. Vacuum (VU): In order to achieve a much higher CO recovery and a complete regeneration effect of the adsorbent bed, additional power consumption of the vacuum pump is often adopted to further depressurize the bed pressure to a lowest level of PL. VI. Equalization−repressurization (ER): After a deeply regenerating the adsorption bed, the bed is bound to repressurize first from PL to PM by countercurrently introducing the light exhaust gas from step ED. VII. Pressurization (PR): Eventually, the bed pressure is repressurized countercurrently to the adsorption pressure value of PH by introducing a partial adsorption exhaust with high-pressure from another bed undergoing the AD step. The clean high-pressure adsorption bed is ready for the next VPSA cycle of repeating steps from (I) to (VII). As depicted in Figure 5, the pressure history profile in a loop cycle was given to present the pressure variation patterns of each VPSA step mentioned previously, and the relevant four kinds of pressure levels were separately fixed to 500 (PH), 250 (PM), 120 (PN), and 20 kPa (PL) in our work. In the further VPSA process studies, the pressure patterns remained

Figure 5. Pressure history profile of the layered adsorption bed in a VPSA cycle.

unchanged. Besides, the feed temperature was set as a desirable normal value of 293.15 K, and considering the adsorption layered-bed filled with pure H2 at feed pressure and temperature prior to the first cycle ran. Meanwhile, in order to evaluate the separation performances of the VPSA process for CO enrichment, four frequently used process performance indicators, product purity, product recovery, adsorbent productivity, and energy consumption, were also defined in Table 6,38−40 and they were adopted in the following VPSA process simulations. Generally, in an industrial layered-bed VPSA unit, the four performance indicators were influenced largely by operating variables such as ratio of AC to MS, feed flow rate, replacement flow rate, and evacuation flow rate. Therefore, precise manipulations for these parameters were needed with the purpose of achieving optimized process operational condition settings. In our work of VPSA process simulations, systematic investigations for manipulative parameters of AC to MS ratio, feed flow rate, replacement flow rate, and evacuation flow rate were separately implemented to analyze the effect sensitivities on the process separation performances and determine the much more optimal values selection. The mathematical model was solved in Aspen Adsorption employing the central finite difference method (CFDM) based on the method of lines (MOL) with 50 discretized elementary nodes and a 1 × 10−5 precision for both absolute and relative tolerances.

4. RESULTS AND DISCUSSIONS 4.1. Breakthrough Curves of Single Adsorption FixedBbed. Experimental and computational results for predicting multicomponent dynamic adsorption behaviors by adsorption breakthrough curve determinations are shown in Figure 6 a−i. The determinations were carried out with a diluted pentabasic syngas feeding composition in 20% helium at a total flow rate of 100 SLPM under 293.15 K and pressure variations from 100 to 6746

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Industrial & Engineering Chemistry Research Table 6. Performance Indicators of the VPSA Process parameter

correlation

∫0

purityCO (%)

tcycle

Fproduct,COyproduct,CO dt t

∫0 cycle Fproduct dt ∫0

recoveryCO (%)

tcycle

∫0

Fproduct,COyproduct,CO dt

tcycle

3600{∫

0

−1

(9)

−1

productivityCO (mol·kg ·h )

Ffeed,COyfeed,CO dt tcycle

(10)

Fproduct,COyproduct,CO dt } tcyclewads

(11a) 2

wads = ρs (1 − εb)πHbD b /4

(11b)

tcycle VinPoutγ [(Pout /Pin)1 − (1/ γ ) − η(γ − 1) 0 tcycle Vproductyproduct,CO dt 0

∫

specific energy consumption (kW·h·Nm3− CO)

∫

1] dt

(12)

Figure 6. Breakthrough curves of a diluted syngas mixture comprising 20% helium on Cu(I)AC (a−c), Cu(I)Y (d−f), and hybrid Cu(I)AC-Y at 293.15 K, 100 SLPM, and pressure up to 500 kPa.

500 kPa on three adsorbent filling patterns. It can be observed that the extended Langmuir adsorption equilibrium model employed in our work can successfully predict the adsorption dynamic behaviors for the multicomponent syngas mixture on both single-bed and layered-bed at different adsorption

pressures when the experimental adsorption isotherms data of pure component are utilized. Additionally, it is also clear to find that each component breakthrough time presents a striking increase with a gradual elevation of operation pressure due to the adsorption isotherm as pressure dependence. It can be 6747

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Figure 7. Effects of layer ratio of AC to MS on the separation performances of VPSA process for (a) purity and recovery and (b) productivity and energy consumption.

further achieved that the adsorbent selectivity for syngas adsorbates follows the strength order of Cu(I)Y > Cu(I)AC-Y > Cu(I)AC according to breakthrough time. It is noteworthy that there is quite a big plateau in the light component of CO2 breakthrough profiles before CO penetration on both Cu(I)Y and Cu(I)AC-Y when compared to that for the Cu(I)AC bed, although a lower CO2 feed concentration of 1% is provided. This reflects that the CO2 extruding profiles at the bed outlet are very sharp owing to the highly nonlinear adsorption isotherms, especially at higher CO2 partial pressures. Besides, this can also be explained as a strong replacement effect by CO penetration profiles, where an increased adsorption selectivity of CO on Cu(I)Y compared to Cu(I)AC induces an independent CO2 concentration peak with a quite delayed CO breakthrough occurrence. Besides, a moderate plateau for CH4 is obvious to be observed just before CO2 as a result of CO2 breakthrough replacement. When it comes to the other remaining lighter components of H2 and N2, it is hard to observe a successive plateau owing to similar adsorption selectivity and higher feed concentration for both and with steeper adsorption breakthrough curve patterns as a result. Nevertheless, it is evident for us to observe that there is a good separation zone between CO and the other four lighter contaminants, which can also be intuitively reflected in each component breakthrough time. Especially, a layered-bed of Cu(I)AC-Y adsorbents packing is more favorable. Consequently, it can be inferred that the first potential contaminant affecting the CO product purity and recovery is CO2, followed by N2, CH4 and finally H2. Finally, it can be concluded from the fixed-bed breakthrough experiments that the mathematical model employed can describe well the adsorption dynamics and equilibrium of syngas system with five components for either single-bed or layered-bed, and it is capable of achieving accurate prediction and application for multibed VPSA system. 4.2. Five-Bed Layered VPSA Process for Syngas Purification. In order to achieve an optimal setting of significant manipulation parameters such as the layer ratio, feed flow rate, replacement flow rate, and evacuation flow rate, we have carried out an optimization procedure for parameter studies. In our parameter optimization work, the procedure was implemented in detail as following. i. First, a CO purity of no less than 99.0% was preferentially specified as a purification target. ii. Second, a variation range of a certain considered manipulation parameter was determined by the dimension and capacity of the VPSA unit.

iii. Third, a certain manipulation parameter value was gradually changed in the limited range when other considered manipulation parameters remained unchanged. Then a detailed analysis was carried out by investigating the responses of the specific manipulation parameter to the process separation performances under a cycle steady state. Further, a favorable value of the specific manipulation parameter was screened out when comparing the responsive results between the four performance indicators with criterions that CO product purity >99.0% and the recovery and productivity to be maximized while the energy consumption to be minimized. Finally, a series of optimized manipulation parameters to be considered could be obtained when employing the optimization procedure from (i) to (iii) for parameter studies. 4.2.1. Effects of the Ratios of AC to MS. The layer ratio is one of the most significant factors to determine the desired product purity, recovery, and productivity as well as energy consumption during heavy component refining by means of the layered-bed VPSA process. Figure 7 presents the influencing results of AC layer to MS layer on the four process performance indicators at 1 m total bed height with ten equal divisions basis when the ratios vary from zero to ten (zero and ten separately represent the bed are completely filled with Cu(I)Y and Cu(I)AC). In all cases, the considered flow rate manipulation variables of feed, replacement, and evacuation are designed to 20.0, 7.0, and 9.0 N m3·h−1, respectively. As we can see from Figure 7a,b, it is the fact that an increase in AC layer proportion can lead to a decrease in product purity and process energy consumption as well as an increase in product recovery and adsorbent productivity. These results indicate that a larger AC layer packing height is capable of obtaining higher CO recovery and productivity with a lower power expense because of its favorable adsorption selectivity and desorption reversibility for CO, but CO purity shows a remarkable decrease tendency form 99.9% (complete MS layer) to 95.5% (complete AC layer) when the ratio is more than 5. It can be concluded that the MS layer serves as a purifier to ensure a higher product purity due to a dominant adsorption selectivity for CO, and a pattern of a larger proportion AC layer with a moderate MS ratio is capable of achieving good separation performances while it deserves to be optimized. This is favorable for selecting a higher AC packing ratio when CO feed concentration shows a decreased tendency in actual working conditions. In addition, we can also observe that the ratio of 7:3 (AC:MS) presents a 6748

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Figure 8. Effects of feed flow rate on the performance indicators of the VPSA process for (a) purity and recovery and (b) productivity and energy consumption.

Figure 9. Effects of replacement flow rate on (a) purity and recovery and (b) productivity and energy consumption in the VPSA process.

appropriate feed flow rate should fall into this range. The favorable value of feed flow rate is 20 N m3·h−1 when a higher 90.0% recovery is to satisfy. 4.2.3. Effects of Replacement Flow Rate. Replacement, namely concurrently rinse or heavy reflux introduced to enhance product purity performance, plays a significant role in balancing product purity and recovery to a satisfactory magnitude level. Figure 9 displays the results of process performance indicators obtained with different replacement flow rates under other restricted conditions of a layer ratio of 7:3 and feed flow rate of 20 N m3·h−1 as well as evacuation flow rate of 9.0 N m3·h−1. With the replacement flow rate varying from a lower level of 2.5 N m3·h−1 to a higher extent of 10.5 N m3·h−1, first a gradual decrease in parabolic pattern and then a rapid decline in rectilinear form are observed similarly for both product recovery and productivity, respectively. This kind of variation behavior derives from a large mass transfer zone height at the beginning of the low replacement flow rate until a significant CO extrusion gradually in the bed outlet at the expense of a decrease in product withdrawal. On the other hand, the product purity shows a logarithmical increase trend, and energy consumption elevates linearly, which are also clear to our easy understanding of variation patterns for replacement flow rate. It can be inferred that the increase amplitude in product purity gradually becomes less sensitive with a prominent elevation for replacement flow rate at the cost of additional compression energy requirements on replacement feeding gas. Especially, when the replacement flow rate is over 7.0 N m3·h−1, the saturated mass transfer zone of CO has

good separation result with a handsome product purity of 99.05% when insignificant degradation of performances in recovery, productivity, and energy consumption. 4.2.2. Effects of Feed Flow Rate. In an industrial VPSA unit, feed flow rate is directly related to the processing capacity for a designed production installation, and further influences the operating cost. It can be seen from Figure 8 that, when the feed flow rate varies from 15 to 25 N m3·h−1 under keeping it unchanged for replacement and evacuation flow rates as well as an optimized layer ratio of 7:3, the product purity, adsorbent productivity, and energy consumption all show a similar monotonically increasing tendency. However, the product recovery decreases linearly as a whole, opposite to other indicators. Increases in both purity and productivity are due to an enhancive utilization factor of adsorption bed along with a profound adsorption saturation quantity of CO, while highpower consumption is required to be inputted in order to obtain a larger feed loading at the expense of significant power increase in both compressor and vacuum pump. Although a higher feed flow rate will improve the performances of product purity and productivity, the degraded CO adsorption front and elevated CO breakthrough are inevitable to cause more CO loss in the adsorption exhaust gas. On the other hand, a distinct phenomenon can be observed that CO purity and recovery show a little flat oscillation when feed flow rate is altered from 20 to 22 N m3·h−1. This implies that the layered-bed still keeps unsaturated by CO at these feed flow rate ranges. Surely, with further increasing feed flow rate over 22 N m3·h−1, the process performances appear to change significantly. It proves that 6749

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Figure 10. Effects of evacuation flow rate on the VPSA process performance: (a) purity and recovery and (b) productivity and energy consumption.

Table 7. Separation Performances Results for the Optimized Layed-Bed VPSA Process material stream

flow rate (N m3·h−1)

CO purity (%)

CO recovery (%)

feed product exhaust off-gas blowdown evacuation replacement

20 3.26 12.75 1.27 4.77 9 7

32.3 99.05 2.98 12.55 99.4 98.9 99.03

100 91.65 5.9 2.45

productivity (mol·kg−1·h−1) 5.122

energy consumption (kW·h·Nm3− CO) 0.125 0 0 0 0 0.028 0.013

Figure 11. Concentration distribution profiles of the gas phase (a) and solid phase (b) for CO obtained along the axial bed at the end of each step under optimized CSS.

h−1 at a 7:3 layer ratio, 20 N m3·h−1 feed flow rate, and 7.0 N m3·h−1 replacement flow rate, the responses of product recovery, adsorbent productivity, and process energy consumption are all observed to increase linearly with a decreased effect in product purity instead. This implies that product purity and other performance indicators are balanced couples deserved to make trade-offs. Such a result is capable of being interpreted as that, although more adsorbed CO product can be recovered from adsorbents with the evacuation flow rate becoming larger, meanwhile significant lighter component contaminants can also be extracted along with CO, which will cause a product purity decrease as a consequence. What is noteworthy is that the product purity is supposed to first increase and then decrease when an elevated evacuation degree is gradually given, which indicates that the evacuation flow rate should be selected within an appropriate range on no more than 7.0 N m3·h−1 if a higher product purity exceeding 99.5% is required. However, this flow rate value should be controlled greater than 8.5 N m3·h−1 on condition that more than 90%

approached toward nearly the bed top, and the product purity hardly obtains an effective upgrade at the expense of the reduced performance behaviors for the other three performance indicators. Therefore, the replacement flow rate of 7.0 N m3·h−1 is preferred to our VPSA process optimization study. 4.2.4. Effects of Evacuation Flow Rate. In the PSA process for recovery of either light or heavy gas components, a stripping PSA employed for lighter component purification and a rectifying PSA utilized for heavier component enrichment are two commonly industrialized adsorption separation processes. The key selection for the adsorption process to develop eventually depends on process economics. The former obtains a deeper adsorbent regeneration effect by purging with lighter components while the latter may be involved in regenerating with power unit input of the vacuum pump, namely the VPSA process. Such a process is strongly affected by the evacuation pressure which is closely related to and even controlled by the flow rate of vacuum pump. As displayed in Figure 10, with different evacuation flow rates changing from 7.0 to 11.0 N m3· 6750

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Figure 12. Concentration variation profiles of the gas phase for (a) CH4 and CO2 and (b) H2 and N2 obtained along the axial bed under optimized CSS.

Compared with significant differences in CO gas and solid phases composition variations, concentration fluctuations for lighter component contaminants in the gas phase show natural change tendencies because of similar adsorption properties on both Cu(I)AC and Cu(I)Y. It is unambiguous for us to see in Figure 12 all of the lighter components except for CO2 reach an adsorption saturation extent in the AC layer at the end of the AD step. It indicates that the AC layer is capable of separating lighter components from CO with a relatively complete level, while CO2 presents a comparatively high adsorption capacity to contaminate CO phase purity. It requires the MS layer to further act as a CO2 purifier when compared with a similar declining breakthrough curve pattern of CO. When it comes to the RP step, steeper light component concentration peaks are also obvious for us to understand indirectly that the CO product replacement effect will render the saturation CO adsorption front to propagate toward the bed top. Nevertheless, compared with the flat desorption curves of the other three light components, the gas phase CO2 concentration variation in the VU step still remains a steep form. It can be inferred that CO2 is likely to be the only one significant contaminant to result in a decrease for both CO product purity and recovery. Except for component concentration variations in the layered-bed, temperature performance is also another vital factor to affect process performances. As depicted in Figure 13 of the history profile in the form of a three-dimensional shaded surface for the adsorption bed at an optimized CSS cycle, dynamic oscillations of axial temperature in the AC layer are much more than those in the MS layer, and the latter only has a small temperature difference of nearly 20 K arising from drastic replacement and evacuation effects. It demonstrates that heat effects of CO adsorption and desorption occur mainly in the AC layer, while the thermal effect of the MS layer results from the bulk lighter components as well as a small quantity of CO. Adsorption and replacement effects will cause the bed temperature to increase rapidly if no heat exchanger is applied. This reflects that the evacuation desorption effect will render a steep decrease in bed temperature in the case of no additional heat input compensating the thermal loss. The high temperature peak value of 350 K and low temperature peak value of 220 K are all highlighting that it is necessary to introduce additional intensive heat exchanger equipment in practical industrial VPSA process design for CO enrichment implementation.

product recovery is demanded, suggesting that an optimized evacuation operation flow rate of 9.0 N m3·h−1can balance product purity and recovery no less favorable than 99.0% and 91%, respectively. 4.2.5. Optimized Process Behaviors. Table 7 presents the results of process performances obtained at cyclic steady state (CSS) under optimization conditions of 7:3 layer ratio, 20.0 N m3·h−1 feed flow rate, 7.0 N m3·h−1 replacement flow rate, and 9.0 N m3·h−1 evacuation flow rate obtained from the abovementioned parameter optimization studies. It demonstrates that good process separation behaviors of 99.05% CO purity, 91.65% CO recovery, 5.122 molCO·kg−1·h−1 of adsorbent productivity, and 0.166 kW·h·N m −3 CO of energy consumption can be obtained simultaneously at an optimized CSS. Process performance indicators of product purity, product recovery, adsorbent productivity, and energy consumption are macroscopic behaviors governed by manipulated variables of layer ratio, feed flow rate, replacement flow rate, and evacuation flow rate discussed above. Good performances can be ascribed to the variations of dependent variables of temperature and concentration inside of the layered-bed with excellent physical distribution properties. Figures 11−13 separately exhibit the results of historical profiles of gas phase, solid phase, and temperature distributions in the layered-bed at optimized CSS for the VPSA process. As we can see from Figure 11a, steeper adsorption breakthrough fronts of CO are both observed during AD and ED steps which ensure a lower CO loss in the bed outlet. Nearly one-half of the AC layer height with CO adsorption saturation can be used to explain it, which is also given in Figure 11b. Followed by RP, BD, and VU steps, no less than 95% CO concentration distributions in the gas phase on average can be observed in the AC layer, but the CO concentration shows a remarkable decreased tendency on MS layer outlet. This implies that Cu(I)Y is much more favorable for CO adsorption compared with Cu(I)AC, and it is the reason why a stepped break occurs in the CO solid phase concentration variation in the transition of adsorbent layer packing type. As a solution, in the following ER and PR steps, excessive lighter components, produced from separately ED and AD steps of bed outlet, are applied to purge and push the CO adsorption front to a lower level as good as that of AD and ED steps end. Although CO solid loading in the MS layer cannot be reduced to a lower level at a once as expected just like AC layer, it contributes to a resultful increase in CO product recovery instead. 6751

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role of purifying CO not to penetrate in the bed top, but tremendous adsorption−desorption heat effects implied that a heat exchanger cost was needed in the factual VPSA process application for CO enrichment from syngas.

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.iecr.7b00229. Detailed diagram information about the experimental adsorption equilibrium apparatus (Figure S1) as well as the fixed-bed breakthrough installation (Figure S2). In addition, detailed equations information on the initial and boundary conditions for breakthrough and VPSA process simulations (Table S1). (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Tel.: +086-022-27892097. E-mail: [email protected]. ORCID

Figure 13. Axial temperature distribution in a layered adsorption bed at optimized CSS.

Donghui Zhang: 0000-0003-4892-2461 Notes

The authors declare no competing financial interest.

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5. CONCLUSION Copper ion impregnating modification for activated carbon and ion-exchange treatment for NaY zeolite were first applied to prepare two reinforced adsorbents of Cu(I)AC and Cu(I)Y, respectively. Then pure component adsorption equilibrium properties of CH4, CO, CO2, H2, and N2 on the two modified adsorbents were evaluated by the Langmuir adsorption isotherm equation from the fittings to experimental data to achieve a perfect validation for higher CO adsorption equilibrium selectivity. Experimental multicomponent breakthrough curves on a single fixed-bed packed with Cu(I)AC, Cu(I)Y, and hybrid layered Cu(I)AC-Y adsorbents separately for a diluted syngas with helium were carried out to predict the multicomponent adsorption dynamics and equilibrium. The predicted results were then proved to succeed in describing the dynamic competitive adsorption performances and validating the accuracy of the fixed-bed mathematical model employed in breakthrough simulations. Furthermore, a pilot-scale five-bed and seven-step layered-bed VPSA modeling system was performed to implement systematically parametric optimization studies of the influences of layer ratio, feed flow rate, replacement flow rate, and evacuation flow rate on process separation performance indicators concerning product purity, product recovery, adsorbent productivity, and energy consumption. The investigation results for parameter sensitivities have revealed the fact that a smaller Cu(I)Y layer filling proportion was more favorable to obtain a good desorption performance. While product purity and recovery satisfied the negative relationships in the flow rate variations range for different operational variables, positive feedback and inverse feedback are applicable to evaluate the relations of recovery vs productivity and energy consumption, respectively. Finally, an optimized VPSA process with good process performances of 99.05% CO purity, 91.65% CO recovery, 5.122 mol·kg−1·h−1, and 0.166 kW·h·Nm−3 CO was analyzed in depth by investigating the distributional behaviors of concentration and temperature inside the layered-bed. It turned out that the separation zone between the light and heavy components almost existed in the AC layer, while the MS layer played the

ACKNOWLEDGMENTS This research is financially supported by the State Key Laboratory of Chemical Engineering of Tianjin University (No. SKL-ChE-16B05) and the Key Laboratory of Ethnic Affairs Commission and Ministry of Education of China (No. KF2015003).

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NOMENCLATURE

English Symbols

bi ci Cpa,i Cps Cpw Cvg Dax,i Db De,i Dk,i Dm,i F hamb Hb hf hw kg kLDF,i ks kw Mi P qi qi* 6752

adsorption affinity (kPa−1) gas phase concentration of component i (mol·m−3) specific heat capacity of adsorbed phase of component i (J·mol−1·K−1) specific heat capacity of adsorbent (J·kg−1·K−1) specific heat capacity of bed wall (J·kg−1·K−1) specific gas phase heat capacity at constant volume (J· mol−1·K−1) axial dispersion coefficient of component i (m2·s−1) bed diameter (m) effective diffusion coefficient of component i (m2·s−1) Knudsen diffusion coefficient of component i (m2·s−1) molecular diffusion coefficient of component i (m2·s−1) molar flow rate (mol·s−1) wall-ambient heat transfer coefficient (W·m−2·K−1) height of adsorbent layer (m) gas−solid heat transfer coefficient (W·m−2·K−1) gas-wall heat transfer coefficient (W·m−2·K−1) gas phase thermal conductivity (W·m−1·K−1) LDF coefficient (s−1) solid thermal conductivity (W·m−1·K−1) thermal conductivity of bed wall (W·m−1·K−1) molar weight of component i (kg·mol−1) pressure (kPa) adsorbed phase concentration of component i (mol· kg−1) adsorbed phase concentration in equilibrium with bulk component i (mol·kg−1) DOI: 10.1021/acs.iecr.7b00229 Ind. Eng. Chem. Res. 2017, 56, 6741−6754

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Industrial & Engineering Chemistry Research qm,i rp Rg t T T0 Tamb Tg Ts Tw vg V wads Wt z

(14) Ma, J.; Li, L.; Ren, J.; Li, R. CO adsorption on activated carbonsupported Cu-based adsorbent prepared by a facile route. Sep. Purif. Technol. 2010, 76, 89. (15) Xie, Y.; Zhang, J.; Qiu, J.; Tong, X.; Fu, J.; Yang, G.; Yan, H.; Tang, Y. Zeolites modified by CuCl for separating CO from gas mixtures containing CO2. Adsorption 1997, 3, 27. (16) Chen, Y.; Ning, P.; Xie, Y.; Chen, Y.; Sun, H.; Liu, Z. Pilot-scale experiment for purification of CO from industrial tail gases by pressure swing adsorption. Chin. J. Chem. Eng. 2008, 16, 715. (17) Li, S.; Yang, H.; Zhang, D. Enrichment of CO from syngas with Cu(I)Y adsorbent by five-bed VPSA. Front. Chem. Sci. Eng. 2013, 7, 472. (18) Yin, Y.; Tan, P.; Liu, X. Q.; Zhu, J.; Sun, L. B. Constructing a confined space in silica nanopores: an ideal platform for the formation and dispersion of cuprous sites. J. Mater. Chem. A 2014, 2, 3399. (19) Qin, J. X.; Tan, P.; Jiang, Y.; Liu, X. Q.; He, Q. X.; Sun, L. B. Functionalization of metal−organic frameworks with cuprous sites using vapor-induced selective reduction: efficient adsorbents for deep desulfurization. Green Chem. 2016, 18, 3210. (20) Hasan, Z.; Jhung, S. H. Facile Method To Disperse Nonporous Metal Organic Frameworks: Composite Formation with a Porous Metal Organic Framework and Application in Adsorptive Desulfurization. ACS Appl. Mater. Interfaces 2015, 7, 10429. (21) Jiang, W.-J.; Yin, Y.; Liu, X.-Q.; Yin, X.-Q.; Shi, Y.-Q.; Sun, L.-B. Fabrication of Supported Cuprous Sites at Low Temperatures: An Efficient, Controllable Strategy Using Vapor-Induced Reduction. J. Am. Chem. Soc. 2013, 135, 8137. (22) Hirai, H.; Wada, K.; Komiyama, M. Active carbon-supported copper(I) chloride as solid adsorbent for carbon monoxide. Bull. Chem. Soc. Jpn. 1986, 59, 2217. (23) Huang, Y.-Y. Selective adsorption of carbon monoxide and complex formation of cuprous ammines in copper(I) Y zeolites. J. Catal. 1973, 30, 187. (24) Nikolic, D. D.; Kikkinides, E. S. Modelling and optimization of hybrid PSA/membrane separation processes. Adsorption 2015, 21, 283. (25) Ahn, H.; Lee, C.-H.; Seo, B.; Yang, J.; Baek, K. Backfill cycle of a layered bed H2 PSA process. Adsorption 1999, 5, 419. (26) Ahn, S.; You, Y.-W.; Lee, D.-G.; Kim, K.-H.; Oh, M.; Lee, C.-H. Layered two- and four-bed PSA processes for H2 recovery from coal gas. Chem. Eng. Sci. 2012, 68, 413. (27) Nam, G.-M.; Jeong, B.-M.; Kang, S.-H.; Lee, B.-K.; Choi, D.-K. Equilibrium Isotherms of CH4, C2H6, C2H4, N2, and H2 on Zeolite 5A Using a Static Volumetric Method. J. Chem. Eng. Data 2005, 50, 72. (28) Moon, D.-K.; Lee, D.-G.; Lee, C.-H. H2 pressure swing adsorption for high pressure syngas from an integrated gasification combined cycle with a carbon capture process. Appl. Energy 2016, 183, 760. (29) You, Y.-W.; Lee, D.-G.; Yoon, K.-Y.; Moon, D.-K.; Kim, S. M.; Lee, C.-H. H2 PSA purifier for CO removal from hydrogen mixtures. Int. J. Hydrogen Energy 2012, 37, 18175. (30) Ribeiro, A. M.; Grande, C. A.; Lopes, F. V.; Loureiro, J. M.; Rodrigues, A. E. A parametric study of layered bed PSA for hydrogen purification. Chem. Eng. Sci. 2008, 63, 5258. (31) Campo, M. C.; Ribeiro, A. M.; Ferreira, A. F. P.; Santos, J. C.; Lutz, C.; Loureiro, J. M.; Rodrigues, A. E. Carbon dioxide removal for methane upgrade by a VSA process using an improved 13X zeolite. Fuel Process. Technol. 2016, 143, 185. (32) Jiang, C.; Gong, X.; Qu, H. Multivariate Modeling and Prediction of Breakthrough Curves for Herbal Medicine Adsorption on Column Chromatography: A Case Study. Sep. Sci. Technol. 2015, 50, 1030. (33) Lopes, F. V. S.; Grande, C. A.; Rodrigues, A. E. Activated carbon for hydrogen purification by pressure swing adsorption: Multicomponent breakthrough curves and PSA performance. Chem. Eng. Sci. 2011, 66, 303. (34) Ntiamoah, A.; Ling, J.; Xiao, P.; Webley, P. A.; Zhai, Y. CO2 capture by vacuum swing adsorption: role of multiple pressure equalization steps. Adsorption 2015, 21, 509.

specific saturation adsorption capacity of component i (mol·kg−1) adsorbent particle radius (m) ideal gas constant (J·mol−1·K−1) time (s) temperature (K) inner temperature of the bed wall (K) ambient temperature (K) gas phase temperature (K) solid temperature (K) bed wall temperature (K) gas phase superficial velocity (m·s−1) standard volume flow rate (m3·h−1) mass of adsorbent layer (kg) thickness of bed wall (m) axial coordinate (m)

Greek symbols

ρg ρs ρw ΔHi μ ψ γ η εb εp τ

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gas phase molar density (mol·m−3) adsorbent density (kg·m−3) density of bed wall (kg·m−3) isosteric heat of adsorption of component i (kJ·mol−1) bulk gas phase mixture viscosity (kg·m−1·s−1) shape factor of adsorbent particle (dimensionless) ratio of specific heats (Cp/Cv) (dimensionless) compressor efficiency (dimensionless) bed porosity (dimensionless) particle porosity (dimensionless) pore tortuosity (dimensionless)

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