Simulation and experimental results of a PSA process for production of hydrogen used in fuel cells

Accepted Manuscript Title: Simulation and experimental results of a PSA process for production of hydrogen used in fuel cells Authors: Amna Abdeljaoued, Frederico Relvas, Ad´elio Mendes, Mohamed Hachemi Chahbani PII: DOI: Reference:

S2213-3437(17)30649-8 https://doi.org/10.1016/j.jece.2017.12.010 JECE 2057

To appear in: Received date: Revised date: Accepted date:

3-8-2017 4-12-2017 5-12-2017

Please cite this article as: Amna Abdeljaoued, Frederico Relvas, Ad´elio Mendes, Mohamed Hachemi Chahbani, Simulation and experimental results of a PSA process for production of hydrogen used in fuel cells, Journal of Environmental Chemical Engineering https://doi.org/10.1016/j.jece.2017.12.010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Simulation and experimental results of a PSA process for production of hydrogen used in fuel cells

AmnaAbdeljaoued1,2, Frederico Relvas3, Adélio Mendes3 and Mohamed Hachemi Chahbani1,4

Ecole Nationale d’Ingénieurs de Gabès, Université de Gabès, Rue Omar Ibn Elkhattab, Zrig, Gabès 6029, Tunisia 3 LEPAE— Laboratory for Process, Environmental and Energy Engineering, Department of Chemical Engineering, Faculty of Engineering, Porto University, Portugal.

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2

Laboratoire Génie des procédés et systèmes industriels (LR11ES54), Université de Gabès, Rue Omar Ibn Elkhattab, Zrig, Gabès 6029, Tunisia

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1

4

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Institut Supérieur des Sciences Appliquées et de Technologie de Gabès, Université de Gabès, Rue Omar Ibn Elkhattab, Zrig, Gabès 6029, Tunisia

Abstract—In recent years, considerable effort has been made to develop technologies for

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harvesting energy from renewable sources. Hydrogen can be used in fuel cells to produce

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electricity very efficiently and cleanly. Among the various processes proposed, steam

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reforming of ethanol for hydrogen production is very attractive. In the steam reforming reaction, in addition to H2 and CO2, significant amounts of CO and CH4 are also formed due

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to side reactions. Prior to design a Pressure Swing Adsorption (PSA) unit aiming at producing H2 with very small amounts of CO, it is essential to experimentally determine the adsorption equilibrium isotherms and mass transfer kinetics data of the multicomponent system (H2, CH4, CO2, CO). A theoretical twelve- step four-column PSA model has been developed for

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studying impurity removal from steam reforming of ethanol for producing ultra-pure hydrogen. The model which is implemented in Aspen Adsorption was validated by

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experimental data on a commercial activated carbon. Simulation results relative to breakthrough curves, bed temperature distribution and PSA performances show good

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agreement with experimental ones. Keywords— H2 production, breakthrough curves, adsorption equilibrium isotherms, simulation, PSA process,

experimental validation

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INTRODUCTION The desire to reduce greenhouse gas emissions and limited resources in supplying fossil fuels have led to demands for diversifying energy resources as a priority [1]. A considerable effort has been expended in developing technologies for using renewable energy sources. Hydrogen is considered as one of the most important environmentally friendly energy carriers. The

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amount of energy produced per unit mass during hydrogen combustion is higher than that evolved by other fuel such as methane, gasoline or coal [2,3]. Hydrogen can be used in fuel

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cells to produce electricity very efficiently and cleanly (i.e. no CO2 emission and high energy yield) [1,4].

Among the various processes proposed for hydrogen production, namely electrolysis of water, steam reforming of hydrocarbons, auto-thermal processes and biological processes, steam

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reforming of ethanol for the production of hydrogen is very attractive because ethanol can be

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produced by fermentation of renewable resources like biomass, is easy to transport and is

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nontoxic. [5,6].

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More importantly, ethanol is CO2 neutral since the amount of CO2 produced by steam reforming is consumed by the biomass growth, and this offers a nearly closed carbon loop and does not contribute to greenhouse gas emissions. In last two decades research interest in the

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area of catalytic steam reforming of ethanol has been increased.

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A further advantage is that, supported by appropriate storage technologies, hydrogen can be utilized for domestic consumption as it can be safely transported through conventional means

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[7,8].

A number of experimental and theoretical studies have been carried out to investigate

hydrogen separation from Steam methane reforming (SMR) off-gas, oven coke gas, or

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refinery fuel gas by Pressure Swing Adsorption (PSA) process using single/layered-bed configuration [1,4,9,10]. In the steam reforming reaction, in addition to H2 and CO2, significant amounts of CO and CH4 are also formed due to side reactions. For use in fuel cells, the CO content has to be reduced to low values; the strong poisoning effect of carbon monoxide in the reformate gas 2

has long been known, and extensively addressed. CO is extremely detrimental to cell performance by dramatically reducing the power output. CO adsorption diminishes the cell’s performance by blocking and reducing the number of catalyst sites available for the hydrogen oxidation reaction. Studies have shown that high concentrations of CO (around 100 ppm) are extremely detrimental to cell performance. On the other hand, 20 ppm CO level has shown negligible

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reduction in fuel cell performance [1,4,11,12]. Pressure swing adsorption units are commonly used in hydrogen purification from

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reforming gas [1,9,13].

Many studies have dealt with the simulation and optimization of adsorption processes [14,15,16,17,18,19].

The focus of current research on PSA is for the most part directed on developing adsorbents

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upon the activated carbons and the zeolites which have already been widely applied in PSA

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sorbent beds [18-20].

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In order to improve the separation efficiency of PSA systems, many versions of PSA process have been developed incorporating multiple beds (2–12 beds) and multilayered

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columns with different types of adsorbents that can produce H2 purity between 98 and 99.999

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%, with 70–90% H2 recovery [21,22,23,24,25].

Typically, large-scale processes employ a large number of beds with several pressure equalizations to better utilize the pressure energy and to increase the H2 recovery. For small

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capacity units where the plant economics are sensitive to capital cost, fewer beds and pressure equalizations may be favored over higher hydrogen recovery.

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The present work deals with the optimization of a PSA unit for obtaining pure hydrogen, to be used in fuel cells, from a gas mixture whose composition is identical to the one of gas exiting

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a steam ethanol reformer. The PSA unit to be considered constitutes a part of a whole system aiming at producing electricity from bioethanol in remote areas with no electrical network. The number of potential applications and end-users, especially in developing countries, is important. The schematic description of the whole system is given in Fig.1. It is composed of a steam ethanol reformer, a gas purification unit, a H2 buffer storage, and a fuel cell system 3

that delivers power to direct current grid. The quality of hydrogen produced by the PSA unit is a key variable affecting the final system cost, efficiency and durability. Compared to diesel generators, fuel cells require less maintenance and produce smaller atmospheric, acoustic and vibrational emissions. It has to be noted that the optimization of the PSA unit will depend on downstream and upstream systems. Thus, the high pressure of the PSA unit will be identical to the pressure of the gas mixture at the reformer exit. This eliminates the need for a

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compressor and of course permits to reduce energy consumption. Small scale units of methanol/ethanol steam reforming typically operate at maximum 10 bars. All the work that is

going to be done should take this into account. The quality of hydrogen produced by the PSA

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unit should also comply with the required purity for use in fuel cells, especially as regards CO

content. Thus, the work to be undertaken relative to the PSA unit, given the constraints imposed by downstream and upstream systems is novel and worth doing. It will surely contribute to the optimization of the whole system for electricity production with fuel cells

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using bioethanol.

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The present paper will focus on the separation of hydrogen from a four component mixture (H2/CO2/CH4/CO) using a twelve step, four-column pressure swing adsorption cycle. The

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target is to optimize the process to reach CO content less than 20 ppm in the purified H2, with more than 75 % of hydrogen recovery. With further optimization,

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even lower CO concentrations are expected. Experiments will focus on making improvements in both the purity and recovery. Details of the optimization procedure to determine the optimal operating conditions of the PSA system are not reported here. The

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optimization of the process of purification of hydrogen by PSA will be done through developing a simulation tool which has to be validated experimentally. Simulations have the benefit of forecasting multiple metrics simultaneously. One change

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may improve the forecast for one metric, but degrade the fit for another. Fortunately, expanding computing power and improving algorithms continue to reduce the time and effort

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to improve product quality and quantity. In practice, PSA units for hydrogen purification use up to three different adsorbent layers.

The first layer reached by the feed mixture, usually a guard bed, is composed of alumina or silica gel to essentially adsorb H2O; the second is composed of activated carbon, which 4

adsorbs CH4, CO, CO2 and traces of sulfur components; and as a third layer, zeolites are used for improved adsorption of CO, N2 and other trace components. Activated carbon is usually preferred to zeolites given its low cost provided that the gas separation can be carried out (as low as 400 US dollars per ton for activated carbon, around 2200 US dollars per ton for zeolite 13X, around 1500 US dollars per ton for zeolite 4A).

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The first step that was carried out, before studying hydrogen purification through a PSA process, was the determination of adsorption equilibriums on a commercial activated carbon and kinetics data of the multicomponent system [1,26]. Breakthrough adsorption

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experiments were conducted in a fixed-bed column with a synthesized gas mixture whose

composition is (1% CO, 25% CO2, 5% CH4 and 69% H2). The synthesized gas has the same composition as the industrial reforming gas. Temperature variations were detected at two positions inside the bed to track the movement of concentration fronts. Feed, purge and

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production flow rates and pressure were also recorded during the PSA cycle.

The

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simulation tool is based on a mathematical model that includes mass, energy and

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momentum balances. It permits to study the dynamic behavior of the adsorption bed and to estimate separation performances of the PSA process and also for optimization purposes.

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Experiment results have been used to assess required parameters model and then to validate the model itself. Thus, the simulation tool would have the ability to simulate and optimize

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the PSA process for producing pure hydrogen used in fuel cells.

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EXPERIMENTAL SECTION

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II.1 MATERIAL AND METHODS II.1.1Material

A buffer tank was used as a feed storage tank to prepare a synthetic gas mixture with a

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composition of 69% of H2, 25 % of CO2, 5 % of CH4 and 1 % of CO; the synthetic gas is

calibrated and tested to avoid pressure fluctuations.

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A commercial activated carbon from Carbo Tech company, which has a particle diameter size ~ 1.202 mm, a real density equal to 1.969 g·cm-3and an apparent density equal to 0.944g·cm-3 is selected for the study. Helium pycnometry is used to determine the real density of the adsorbent measured using a gravimetric unit described elsewhere [27] while a quantachrome mercury porosimeter, model POREMASTER 60, is used to determine apparent density. The method apparatus is described

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elsewhere [28]. II.1.2 Methods II.1.2.1 Equilibrium isotherms

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The determination of carbon monoxide, carbon dioxide, methane and hydrogen equilibrium isotherms was conducted using the volumetric method. This technique is based on pressure variation of the relevant gas after an expansion. Assuming the ideal gas behavior and knowing described

elsewhere

[29],

now

equipped

a

pressure

transducer

of

350

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mbar (Druck, PMP 4010, (0.08% FS).

with

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the pressure drop, it is possible to determine the gas quantity adsorbed. The apparatus used is

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Single-component adsorption isotherms of CO2, CO, CH4, and H2 on commercial activated

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carbon were measured at different temperatures 30, 40, and 50 °C and pressure up to 7 bar using fresh adsorbent. Experiments for obtaining the isotherms were preceded by heating up

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the sample inside the relevant vessel at 90 °C, using helium followed by a vacuum of <0.01 mbar.

II.1.2.2 Breakthrough apparatus

To measure the adsorption data of multicomponent mixtures, an experimental setup

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composed of one column filled with the commercial activated carbon is used. The breakthrough experimental setup is shown in Fig.2. The column is equipped with two

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thermocouples (T), two pressure transducers (P) (Druck PMP 4010, with pressure range variation from 0 to 7 bars (±0.08 % FS), a mass flow meter controller (FC), for the feed flow

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(Bronkhorst El-flow, 100 mLPTNmin-1 and a range of 0-2 LPTN min-1, ± 0.5 % Rd plus ±0.1 % FS), a mass flow meter (FM) for measuring the exit flow (Bronkhorst El-flow, 0-10 LPTN

min-1 ±0.5 % Rd plus ±0.1 % FS) and a vacuum pump (Ilmvac, MPC201T) for the desorption step. To ensure an isothermal operation the setup is placed inside a thermostatic chamber. The

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composition of the gas of the outlet flow was determined using mass spectrometer. The apparatus is described elsewhere [30]. The bed characteristics are summarized in Table 1. II.1.2.3 PSA unit The lab-scale of the pilot PSA system is shown in Fig. 3. To measure the PSA data of multicomponent mixtures, a PSA unit composed of four columns which have an inner diameter of 2.2 cm and a length of 27.5cm was used. They were filled with ~89.73 g of a

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commercial activated carbon.

Two thermocouples type K were installed at positions 2.5 and 25 cm from the feed end to

measure temperature variations inside the adsorption bed, four pressure transducers (Druck,

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PMP 4010, 0 – 10 bar) were located in each column to measure the pressure history during operation.

The feed section was equipped with four Bronkhorst mass flow controllers’ series F201C

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and F201CV, which are connected to methane, carbon monoxide cylinders and to the carbon dioxide and hydrogen gas grid of the laboratory.

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A 3 liters buffer tank was used as a feed storage tank to prepare a synthetic gas mixture

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with a composition of 73 % of H2, 25 % of CO2, 1 % of CH4 and 1 % of CO, to avoid

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pressure fluctuations.

The system was automatically operated by a personal computer that was interfaced with

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Lab View software with a built-in control program. Solenoid valves were activated according to a process time schedule. Eight solenoid AscoSCE37CA056nv were placed in the feed and off-gas side (4 for each side), SC G325B031were used to control the equalization steps and SC G325B031 were placed in product and purge lines positions. To prevent dispensable positions.

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reverse flow Some Swagelok poppet check valves, SS-2C-1/3 were installed at the proper

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The amounts of gas flow into the PSA system was monitored with a mass flow meter (Bronkhorst High-tech, El Flow F-112AC, 0 – 20 dm3·min-1) , the purge flow rate was

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measured using a mass flow meter (Bronkhorst High-tech, El Flow F-111C, 0 – 2 dm3·min-1) and the product flow rate was controlled using a mass flow controller (Bronkhorst High-tech, El-Flow F-201CV, 0 – 10 dm3·min-1). The purge flow rate was regulated using a needle valve. Product samples were passed through the sampling line that was connected to the product tank and were analyzed using a gas chromatograph DANI GC1000. Carbon monoxide 7

composition was measured by a gas analyzer Signal Instruments 7100FM equipped with two sensors for different ranges, from 0.1 ppm up to 5000 ppm. Carbon dioxide was analyzed by a Thermo Scientific Analyzer model 410i. All of the measured data were monitored and saved on the interfaced computer through and AD converter.

MATHEMATICAL MODEL

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A PSA cycle is a sequential combination of elementary pre-defined steps where the

regeneration is made by reducing the total pressure of the bed. A generic mathematical model employed in the description of the behavior of a PSA process should couple mass, momentum

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and energy balances over a packed bed with the appropriate boundary conditions for each of the steps comprising the cycle.

The development of a complete mathematical model capable to describe the dynamic

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behavior of multicomponent adsorption in a fixed bed was based on the following

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assumptions.

III.1 Model assumptions

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The dynamic behavior of the adsorption bed and the PSA process is described by a mathematical model which couples mass, momentum and energy balances over a packed bed.

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The following assumptions were adopted [12,13,16,31,32,33]. -

The ideal gas behavior.

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The multicomponent adsorption equilibrium is represented by the dual site Langmuir

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isotherm.

There are no radial variations in temperature, pressure, concentration, or velocity.

-

The adsorption rate is approximated by a linear driving force (LDF).

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-

The pressure drop through the bed during adsorption and purge steps is described by Ergun’s Equation.

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-

Non-isothermal energy balance with gas and solid conduction.

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III.2 Model equations The mass balance equations for each component and for the total mass of mixture are presented by eqs (1) and (2), respectively.

 2 ci ci c (1   bed ) qi u  DL 2   u i  ci  p 0 t z z  bed t z

(2)

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(1   bed ) n qi u C   p  0 z T  bed i 1 T

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C

(1)

Where u is the gas interstitial velocity, ci is the gas concentration of component i, p is the particle density, bed is the bed porosity, qi is the adsorbed concentration of component i, C is total concentration in gas phase, T is temperature, z is axial co-ordinate and DL is the axial dispersion

(3)

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1 D L =0.7D m + d p u 2

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coefficient, which can be estimated by eq. (3) [34].

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from the Chapman–Enskog equation.

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Where dp is the particle diameter and Dm is the molecular diffusivity that can be calculated

The adsorption rate is given by the linear driving force (LDF) model represented by eq. (4)

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[13,21].

(4)

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qi  ki (qi*  qi ) t

Where ki is the mass-transfer coefficient; qi*is the loading of component i, and can be

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calculated by eq. (5) which describes the dual-site Langmuir isotherm model:

qi* 

qm1i B1i Pi n

1   B1i Pi i 1



qm 2i B2i Pi n

1   B2i Pi

(5)

i 1

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Where qm1i, qm2i are the saturation capacities at site 1 and 2, respectively, B1i and B2i are the affinity parameters for site 1 and 2, respectively, which are considered to be temperaturedependent as expressed in eqs (6) and (7), T is the evaluated temperature, R is the universal

k 2i ) T k ex p ( 6 i ) T

B1 i  k 3 i ex p (

(6 )

B2i  k5i

(7 )

k1i  k 3 i q m 1i

(8 )

k 4i  k5i qm 2i

(9 )

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constant of gases and Pi is the partial in the gas phase.

 H 1ads (1 0 ) R  H 2 ads k6i  (1 1 ) R The parameters k1i, k2i, k3i, k4i, k5i, k6i were optimized by a nonlinear procedure to give the best

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k 2i 

fit to the experimental adsorption isotherms of pure H2, CH4, CO and CO2 , taking the

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  (q j 1

 q mod, i ) 2

(12)

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T1

exp, i

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SS (%) =

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minimum sum of squares (SS) at the three evaluated temperatures, as follows in eq (12):

Where qexp,i and qmod,i are the experimental and predicted amounts adsorbed, respectively, T1

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to T3 are the three tested temperatures, j is the number of points per isotherm and gas component, i represents the component (H2, CH4, CO and CO2 ) and N is the total number of experimental data points.

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For the simulation, it was assumed that the heat of adsorption on the first and second sites are equal: H1,ads  H 2,ads .

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In general a multi-component Langmuir-type adsorption is used to describe the fractional

occupancies. For thermodynamic consistency, the saturation loading for all components must be equal in the multi-component Langmuir model [35].The extended dual-site Langmuir

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(DSL) isotherm model is used to predict the multi-component equilibrium behaviors on activated carbon. The DSL model is effective because it provides a flexible mathematical form to correlate the pure-gas isotherms [36,37].

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Assuming that the gas phase and the solid phase are in thermal equilibrium, the energy balance equation for the bed can be expressed as in eq. (13) [38,39]:

KL

N q j h  2T T T  cC   C  cC u   (  (1  )  )  (1  )  +2 i (Tw -T) bed pg bed p ps bed pg bed p  H j 2 t z t ri z j 1

Where KL, Cpg and Cps are The effective axial thermal conductivity, the specific heats of the gas and solid phases, Tw, hi and ri are wall temperature, inner column wall heat transfer

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coefficient and the column inner radius, respectively; and ∆Hj is heat of adsorption of species j.

The energy balance equation per unit length of column wall can be written as in eq.(14) [38,

wCpw AW

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39]:

Tw  2 rh i i (T Tw )  2 ro ho (Tw Tamb ) t

(14)

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Where ρw, Cpw and Aw are the density, specific heat and the wall cross-section area of the

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wall, respectively.Tamb is the ambient temperature; ho and ro are the heat transfer coefficient

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with outer column wall and outer radius of column wall.

The parameters appearing in the foregoing energy balances are evaluated as follows: the

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heat transfer coefficient between the gas and the adsorbent particles (HTC) is calculated using the correlation of Wakao and Kaguei [40] for the feed conditions and assumed to be approximately constant (the values of HTC used to run the simulations is 799 W m- 2 K-1; the heat transfer coefficient between the gas and the adsorber wall (hw) is estimated using the

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correlation proposed by Yagi and Kunii for cylindrical packed beds [41] for the feed conditions and assumed to be approximately constant (the values of hw used to run the

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simulations is 38 W m- 2K-1 ); the heat transfer coefficient between the outer wall of the adsorber and the environment (Hamb) is assumed approximately constant; the same value of

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Hamb=12 W m- 2K-1 is used to run the simulations.

The Ergun equation given by eq. (15) was used as a simplified momentum balance equation

to calculate the pressure drop along the bed [42, 43].

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(13)



 g u 2 (1   bed )  u (1   bed ) 2 p  150  1.75 z d p  bed d p 2  2bed

(15)

Where ρg is the bulk gas density, µ is the gas viscosity and u is the gas interstitial velocity. Cycle performances are evaluated according to the common parameters of H2 purity and H2 recovery as expressed in eqs (16) and (17). t AD

u |Z  L dt

AD

i

0

(16)

  Cu| i

Z L

dt

t AD

H 2 R ecovery [% ] =



C H 2 u | Z  L dt 

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H2

0

tPR

0



C H 2 u | Z  L dt

0

t AD



C H 2 u | Z  0 dt

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C

(17)

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H 2 Purity [%] =

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III.3 PSA simulation

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The models presented in Section 2 were applied for the simulation of a four-column PSA process with twelve elementary steps. The cycle sequence considered is schematically represented in Fig. 4 as follows:

Adsorption at high pressure (AD)

(ii)

First depressurizing pressure equalization, (FDPE) with the bed which is in the second

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(i)

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pressurizing pressure equalization, (SPPE) down to an average pressure between the beds.

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(iii)

The purge flow is generated by reducing the column pressure starting from a pressure at the end of the first depressurizing pressure equalization step to a pressure that can be chosen at an operator’s disposal. The equilibrated pressure at the second depressurizing pressure equalization step is subject to decision how much purge flow is generated during the providing purge step, (PP). 12

(iv)

Counter current second depressurization pressure equalization, (SDPE) with the bed which is in the first pressurizing pressure equalization (FPPE) down to an average pressure between the beds.

(v)

Blowdown to atmosphere (BD).

(vi)

Purge, (PG) with the gas produced during the providing purge step (PP).

(vii) First pressurizing pressure equalization, (FPPE) with the bed which is in the second

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depressurizing pressure equalization, (SDPE) up to an average pressure between the beds.

(viii) Second pressurizing pressure equalization, (SPPE) with the bed which is in the first

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depressurizing pressure equalization, (FDPE) up to an average pressure between the beds. Idle step all valves are closed.

(x)

Feed pressurization (PR) of a partially pressurized bed by a previous PPE step.

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(ix)

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The step configuration in which the supply purging step is located between the two DPE

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steps has a clear advantage over a cycle in which the supply purging step follows the two DPE steps . In this case it can increase the purge rate to a greater extent since the purging step

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of supply may begin at a higher pressure [44].

The cyclic sequences of four-bed PSA process with step times are shown in table 2.During the

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first pressurizing equalization step a considerable amount of hydrogen is released from the column, which is used for purging the column which is in the purge step.

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The dynamic adsorption bed and the PSA simulation are carried out using Aspen Adsorption V8.0 software.

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Aspen Adsorption simulates gas processes with adsorption only, or adsorptive reaction gas processes where both reaction and adsorption occur simultaneously.

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Gas-phase adsorption is widely used for the large-scale purification or bulk separation of air, natural gas, chemicals and petrochemicals. Aspen Adsorption presents an innovative new modeling approach to maximize profitability in the design, simulation, and optimization of periodic adsorption processes for gas separation, 13

such as Pressure Swing Adsorption (PSA), Thermal Swing Adsorption (TSA), Vacuum Swing Adsorption (VSA), etc. Aspen Adsorption models offer an extremely efficient design tool that can be more readily used as an optimization package to determine optimal design and operating conditions for an

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adsorption process based on the values of purity and recovery desired [45].

Aspen Adsorption uses a set of partial differential equations (PDEs), ordinary differential

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equations (ODEs) and algebraic equations, that represent the mass, momentum and energy

balances, the kinetic and the equilibrium models, together with the appropriate initial and boundary conditions, to fully describe the adsorption process .Spatial derivatives are discretized using algebraic approximations, resulting in a set of ordinary differential equations

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and algebraic equations (DAEs).

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Thus the set of partial differential equations given in the paper relative to mass, heat and

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momentum balances is effectively numerically solved by ASPEN Adsorption software. One

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can change the set of partial differential equations if other assumptions are considered. We have studied the effect of increasing the number of nodes from 10 to 120 nodes; it has simulation results.”

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been found that increasing the number of node more than 60 has a negligible effect on The spatial discretization utilizing 60 upwind differenced nodes adequately, resolves spatial

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gradients across the bed.

Comparison between experimental and simulation results for different numbers of nodes (40 nodes, 60 nodes and 80 nodes) are shown in Fig.5 and Fig. 6 giving the breakthrough

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curve of CH4 and the temperature profiles in the top and the bottom of the column. It can be clearly noted that the simulation results obtained by using spatial discretization of

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60 and 80 nodes coincide. Thus, the choice of a number of nodes equal to 60 ensures both accuracy of results and computation time saving. The spatial derivatives for the adsorption bed model were discretised using the upwind differencing scheme 1 method (UDS1, first order), dividing the axial coordinate of the 14

adsorption bed into 60 nodes. Further details of the simulation environment can be found elsewhere [45].

RESULTS AND DISCUSSION IV.1 Adsorption equilibria

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Single-component adsorption isotherms of CO2, CO, CH4, and H2 on commercial activated carbon were measured at different temperatures 30, 40, and 50 °C using fresh adsorbent. Experiments for obtaining the isotherms were preceded by heating up the sample inside the

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relevant vessel at 90 °C, using helium followed by a vacuum of <0.01 mbar.

Given the expertise on equilibrium isotherms, it is possible to know in advance which model is to be used to correctly represent the experimental data. This permits to save a lot of time.

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However, several models can give the same satisfaction. Langmuir-Freundlich (L-F) model

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and Langmuir model (not reported here) and dual site Langmuir model (D-S) have been tested. For example, Fig.7 gives the fitting of Langmuir-Freundlich and dual site Langmuir

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clearly that both models are satisfactory.

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models to the experimental adsorption isotherm of CO at three temperatures. It appears Aspen Adsorption has a comprehensive list of adsorption isotherm models. In adsorber

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design, we are usually interested in the adsorption equilibria of mixtures, rather than those of pure components. This is because adsorbed gas components interact on the solid surface, so individual gas components adsorb in a different fashion when mixed with other components. Mixture adsorption equilibria data are not readily available. Although measurements can be

EP

made, they are tedious and time-consuming to perform, so it is common practice to predict

CC

mixture isotherms from pure component isotherms. Several methods for predicting mixture isotherms from pure component data have been

A

proposed recently by aspen adsorption software, including:    

Vacancy Solution Extended Langmuir Approach IdealAdsorbed Solution Real Adsorbed Solution Theory 15

Dual-site Langmuir model has been used to predict mixture isotherms; it is used with satisfaction to correlate multi component equilibrium isotherms as reported in literature. The adsorption equilibrium of multi-component model was predicted by dual-site Langmuir model based on monocomponent experimental isotherms data. The adsorption equilibrium isotherms for CH4, CO, CO2 and H2 on commercial activated carbon are plotted in Figures 8

IP T

to 11.Dual-site Langmuir parameters, heats of adsorption and linear driving force parameters are summarized in Table 3.

According to the adsorption isotherms (Fig. 8 to Fig.11) the adsorbed amount increases with

SC R

the pressure and the order of adsorption capacity for components was CO2 >> CH4> CO>> H2. For the activated carbon, the adsorption isotherm of CO2 was much greater and sharper

than the isotherms of the other components. The H2 isotherm has almost a linear form. The adsorption isotherms of these components on activated carbon were affected by temperature

U

variations caused by a relatively weak adsorption. The components adsorption capacities on

M

IV.2.1 Concentration profile

A

IV.2 Breakthrough dynamics of the adsorption bed

N

activated carbon decreased as the temperature changed from 30°C to 50°C.

TE D

A gas mixture (CO: CO2:CH4:H2: He = 1:25:5:20:49 mol. %) was fed to an adsorption bed containing a commercial activated carbon. The breakthrough experiment was carried out at 40 °C, 1 bar and using a feed flow rate of 0.5 L/min. The bed was initially filled with pure helium.

EP

The comparison of the simulation results with the experimental data is reported in Fig. 12. H2 adsorbs only slightly. The first contaminant to break through the column is carbon

CC

monoxide, followed by methane and finally by carbon dioxide. The breakthrough times of CO, CH4 and CO2 are close to 180 s, 500 s and 870 s respectively.

A

The dynamic mathematical model could predict satisfactorily the experimental breakthrough curves. Nevertheless, breakthrough curves for CH4 and CO2 are predicted with small delays of about 10 and 17 s respectively. IV.2.2 Temperature profile 16

The temperature evolution with time at the two bed positions is given in Fig. 13. At the exit of the column, the two peaks of temperature appearing at nearly 500 s and 870 s correspond to the adsorption heat generated by moving concentration fronts of CH4 and CO2 respectively. Due to the low amount of H2 adsorbed and the low concentration of CO in the feed mixture, the temperature peaks corresponding to these components are not detected. The temperature displays only one peak

IP T

evolution just after the bed inlet (2.5 cm from the bed inlet)

corresponding to all components of the mixture; the reason is that at this axial position,

concentration waves of the different components are not yet clearly separated, thus leading to

SC R

the formation of only one temperature front.

The temperature predicted by the mathematical model is in reasonable agreement with the experimental results. The simulated temperature change with time at the two positions of the bed exhibit the same trends of evolution with differences in temperature not exceeding 2 °C.

U

Once saturated, the packed bed temperature decreases gradually due to heat transfer with both

N

gas mixture and column-wall.

A

This slight difference between simulation results and experimental data could be attributed to many factors such as gas analyzer and temperature sensors precision, the estimated variables

M

from literature …

It should be mentioned that one could ameliorate the simulation results by varying the value

TE D

of estimated variables till it fits the experimental data, however the values of these variables

EP

may be wrong and do not reflect reality.

IV.3 Pressure Swing Adsorption process

CC

A PSA experiment according to the cycle described previously was carried out to validate

the mathematical model. A four-column PSA experiment was performed using cycle

A

scheduling illustrated in Fig. 4. The experimental run was performed at ambient temperature at a feed flow rate of 1.37 L/min and the following composition: 73% of H2, 25% of CO2, 1% of CH4 and 1% of CO. The feed pressure was fixed at 8.5 bars and the blowdown pressure at 1 bar. The experimental conditions are shown in table 4. 17

The pressure history for one cycle is shown in Fig. 14. The molar flow rate histories for feed, purge and production obtained during four cycles are reported in Figs. 15, 16 and 17. One can note the good agreement between simulation and experimental results for all the molar flow rates. Simulation results for the same case indicate that a hydrogen purity of 99.9913 % (with 84.39 ppm of CO, 0.17 ppm of CO2 and 0.9 ppm of CH4) and a hydrogen

IP T

recovery of 75.5 % could be obtained.

SC R

IV.3 Parametric analysis

The operating conditions of a H2 PSA process are considered as the key factors, as they have critical influence on process performance. A parametric analysis is performed based on four

U

operating parameters (i.e. adsorption pressure, purge to feed (P/F) ratio, production flow rate

N

and production time). Fig. 18 illustrates the effect of adsorption pressure, P/F ratio, production

A

flow rate and production time on hydrogen purity and recovery of the PSA process. For the parametric analysis, reference values retained of adsorption pressure, P/F ratio,

M

production flow rate and production time are 8.5 bars, 0.1327, 0.8 L.min-1and 100 seconds respectively. The investigated parameter is varied around the reference value, while all the

TE D

others are kept constant.

Fig. 18 gives the variation of the purity and the recovery of hydrogen and CO concentration with the operating parameters mentioned above. It can be noted that purity and recovery vary

EP

in the opposite direction for all the operating parameters studied. Thus, the choice of the value of the parameter will surely depend on the objective to be reached, i.e. the maximization of the purity or the recovery. For the case studied, that is production of pure hydrogen for use in

CC

fuel cells, importance must be given to purity. As shown in Fig. 18 (a), purity increases and recovery decreases when increasing adsorption

A

pressure for a P/F ratio of 0.13, a production flow rate of 0.8 L/min and a production time of 100 seconds. The decrease in recovery is relatively significant when the adsorption pressure changes from 7 to 10 bars. In fact, recovery diminishes from 78 to 71%. The increase in adsorption pressure engenders higher retention of the different components of gas mixture in the column, necessitating more H2 for the purge step, resulting in decreased product recovery 18

ratio. In order to obtain a H2 purity of about 99.99% and recovery ratio higher than 75%, the adsorption pressure of the feed gas should be higher than 8 bars. The highest purity (99.9997%) is obtained for 10 bars; nevertheless the recovery for this pressure is 71.38%, a value which is far from the target one. The effect of the adsorption pressure on the product CO concentration is shown in Fig. 18 (b). It can be seen clearly that decreasing the adsorption pressure is not recommendable for

IP T

lowering the CO concentration in the gas product. Indeed, CO content increases from 3.29 ppm to 538.69 ppm with the decrease of adsorption pressure from 10 bars to 7 bars. The

adsorption pressure should be maintained at a value higher than 8 bars so as to obtain a CO

SC R

concentration lower than 20 ppm.

The effect of the P/F ratio on purity, recovery and CO concentration is given respectively in Figs. 18 (c) and 18 (d). It is obvious that increasing the P/F ratio leads to the increase of purity

U

and the decrease of product recovery due to the use of a higher quantity of pure product for

N

purge. When the P/F ratio varies from 0.10 to 0.16, the improvement in purity and CO

A

concentration are significant; the purity increases from 99.9886% to 99.9981%. By contrast, the CO concentration drops from 95.6 ppm to 14.78 ppm. In addition, the recovery diminishes

M

almost linearly to attain a value of 73.3%.

Figs. 18 (e) and 18 (f) shows respectively the influence of the production flow rate on

TE D

hydrogen purity, recovery and CO concentration. The increase of the production flow rate results in a great improvement of H2 recovery. When the production flow rate varies from 0.7 to 0.9 L/min, H2 recovery increases from 71.49% to 77.47% and CO concentration increases %.

EP

from 8.82 ppm to 123.74 ppm. This leads to the decrease of purity from 99.9991 to 99.9872 The effect of production time on H2 purity, recovery and CO concentration is shown in

CC

Figs. 18 (g) and 18 (h). It can be noted that the increase of the production time leads to higher CO concentration in the product, lower H2 purity and higher recovery. The highest recovery,

A

77.4% H2, could be obtained for a production time of 120 seconds, with a CO concentration of 95.6 ppm and a purity of 99.9896%, these values are respectively lower than 14.78 ppm and 99.9986% obtained for a production time of 80 seconds with a recovery of 70.86%. The variations of the CO concentration are similar to those obtained when the production flow rate is changed. This is because increasing either the production flow rate or the production time, 19

all the other parameters are maintained unchanged, causes more CO to break through the bed (even though very small quantities of CO break through) resulting in a decrease in purity. Further optimization work should be carried out in order to obtain both purity higher than 99.999% and a H2 recovery higher than 75%. The amelioration of the process performance can be achieved by the use of more efficient adsorbents, by testing other PSA operating

IP T

schemes, as well as, by the optimization of the operation parameters of the process. However, one should keep in mind that pressure swing adsorption processes offer great flexibility at

SC R

both design and operational stages, requiring careful selection of important decision variables.

U

CONCLUSION

N

In this work, single-component adsorption isotherms of CO2, CO, CH4, and H2 on commercial activated carbon were measured at different temperatures. The adsorption

A

equilibrium of multi-component model was predicted by dual-site Langmuir model based on

M

mono-component experimental isotherms data.

The breakthrough and PSA experiments were simulated using Aspen AdsorptionV8.0.A

TE D

rigorous mathematical model that includes mass, energy and momentum balances was employed to simulate the dynamic behavior of the adsorption column. Good agreement between simulation results and the experimental data has been achieved for both breakthrough curves and temperature profiles.

EP

A theoretical four-bed PSA model has been developed for studying impurity removal from steam reforming of ethanol for producing hydrogen used in fuel cells. A twelve-step four-column PSA

CC

experiments were performed at ambient temperature. The pressure history and molar flow rates histories obtained from cyclic PSA simulation show a good agreement with the

A

experimental PSA data. A parametric analysis is performed based on four operating parameters (i.e. adsorption pressure, (P/F) ratio, production flow rate and production time).

20

Purity increases and recovery decreases if adsorption pressure and P/F ratio are increased. By contrast the increase of the production time and the production flow rate leads to lower H2 purity and higher recovery. To obtain a CO concentration lower than 20 ppm (99.999% of purity), it seems difficult for the moment to optimize the process so as to get a product recovery higher than 75%. Future

IP T

work will focus on the optimization of the process to achieve this goal.

Nomenclature: Aw , cross-section area of the wall (cm2)

SC R

B1i ,B2i , the affinity parameters for site 1 and 2 C , total concentration in gas phase (mol.cm-3)

ci , the gas concentration of component I (mol.cm-3)

N

Cpg , the specific heats of the gas (J.kg-1 K-1)

U

CH2 , hydrogen concentration (mol.cm-3)

Cpw , the wall specific heat (J.kg-1 K-1) Dm , the molecular diffusivity (m2.s-1)

M

DL , the axial dispersion coefficient (m2.s-1)

A

Cps, the specific heats of the solid phases (J.kg-1 K-1)

TE D

dp , the particle diameter (mm)

Hamb , heat transfer coefficient between the outer wall of the adsorber and the environment (W m-2 K-1)

EP

HTC , the heat transfer coefficient between the gas and the adsorbent particles (W m-2) hi , inner column wall heat transfer coefficient (W m-2) ho , the heat transfer coefficient with outer column wall (W m-2)

CC

hw , the heat transfer coefficient between the gas and the adsorber wall (W m-2) i , represents the component (H2, CH4, CO and CO2 )

A

j , the number of points per isotherm and gas component ki , the mass-transfer coefficient (s-1) KL, The effective axial thermal conductivity (J.m-1 K-1 s-1) k1i, k2i, k3i, k4i, k5i, k6i, parameters of the dual site model L, bed length (cm) 21

N, the total number of experimental data points NC , the total number of components Pi , the partial pressure in the gas phase (bar) qexp,i , the experimental amounts adsorbed (mol.kg-1) qi , the adsorbed concentration of component i (mol.kg-1) qi*, the loading of component i (mol.kg-1)

IP T

qm1i, qm2i , the saturation capacities at site 1 and 2 (mol.kg-1) qmod,i , the predicted amounts adsorbed (mol.kg-1)

SC R

R, the universal constant of gases, 8.314 J/mol/K ri , the column inner radius (cm) ro , outer radius of column wall (cm)

U

T , temperature (K)

N

T1 to T3 , the three tested temperatures (K) Tw , wall temperature (K)

tPR , pressurization time (s)

TE D

z , axial co-ordinate

M

Tamb , the ambient temperature (K)

A

tAD , adsorption time (s)

Greek letters bed , the bed porosity

EP

u , the gas interstitial velocity (cm.s-1) ρg , the bulk gas density (cm3.g-1)

CC

p , the particle density (cm3.g-1) ρw , the wall density (cm3.g-1)

A

∆Hj , heat of adsorption of species j (J.mol-1) ∆H1,ads, ∆H2,ads , the heat of adsorption on the first and second sites (J.mol-1)

22

IP T SC R

REFERENCES

A

CC

EP

TE D

M

A

N

U

[1] Qinglin Huang, Mladen Eic. Simulation of Hydrogen Purification by Pressure Swing Adsorption for Application in Fuel Cells. Elsevier 2010. [2] Abbas HF, Wan Daud WMA. Hydrogen production by methane decomposition: a review. Int J Hydrogen Energy 2010; 35:1160–90. [3] Kim T, Jo S, Song Y-H, Lee DH. Synergetic mechanism of methanol–steam reforming reaction in a catalytic reactor with electric discharges. Appl Energy 2014; 113:1692–9. [4] Filipe V.S.Lopes, Carlos A. Grande, Alı´rio E. Rodrigues .Activated carbon forhydrogen purification by pressure swing adsorption: Multicomponent breakthrough curves and PSA performance. Chemical Engineering Science 66 (2011) 303–317. [5] Kapdan IK, Kargi F. Bio-hydrogen production from waste materials. Enzyme Microb Tech 2006; 38:569–82. [6] Navasa M, Yuan J, Sundén B. Computational fluid dynamics approach for performance evaluation of a solid oxide electrolysis cell for hydrogen production. Appl. Energy 2015;137:867–76. [7] Hord J. Hydrogen safety: an annotated bibliography of regulations, standards and guidelines. Int J Hydrog Energy 1980:579–84. [8] Das D, Veziroglu TN. Advances in biological hydrogen production processes. Int J Hydrog Energy 2008; 33(21):6046–57. [9] Malek A, Farooq S. Hydrogen purification from refinery fuel gas by pressure swing adsorption. AIChE Journal 44. 1998, 1985–1992. [10] José A. Delgadoa, A.E. Rodrigues. Analysis of the boundary conditions for the simulation of the pressure equalization step in PSA cycles. Chemical Engineering Science 63 (2008) 4452 -4463. [11] Majlan EH, Daud WRW, Iyuke SE, Mohamad AB, Kadhum AAH, Mohammad AW, et al. Hydrogen purification using compact pressure swing adsorption system for fuel cell. Int J Hydrogen Energy 2009; 34:2771–7. [12] You Y-W, Lee D-G, Yoon K-Y, Moon D-K, Kim SM, Lee C-H. H2 PSA purifier for CO removal from hydrogen mixtures. Int J Hydrogen Energy 2012; 37:18175–86. 23

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IP T

[13] Lee, J.-J., Kim, M.-K., Lee.D.-G., Ahn,H , Kim,M.J.Lee, C-H, 2008 Heat exchange pressure swing adsorption process for hydrogen separation. AICHE Journal 54, 2054-2064. [14] Cen P, Yang RT. Bulk gas separation by pressure swing adsorption. Ind Eng Chem Fundam 1986; 25(4):758e67. [15] Schell J, Casas N, Marx D, Mazzotti M. Precombustion CO2 capture by pressure swing adsorption (PSA): comparison of laboratory PSA experiments and simulations. Ind Eng Chem Res 2013; 52(24):8311e22. [16] Lopes FVS, Grande CA, Rodrigues AE. Activated carbon for hydrogen purification by pressure swing adsorption: multicomponent breakthrough curvesand PSA performance. Chem Eng Sci 2011; 66(3):303e17. [17] Chahbani M.H., Tondeur D. Predicting the final pressure in the equalization step of PSA cycles. Sep Purif Technol, 2010;71(2):225e32. [18] Rezaei F, Subramanian S, Kalyanaraman J, Lively RP, Kawajiri Y, Realff MJ. Modeling of rapid temperature swing adsorption using hollow fiber adsorbents. Chem Eng Sci 2014;113(3):62e76. [19] Mulgundmath V, Tezel F.Optimisation of carbon dioxide recovery from flue gas in a TPSA system. Adsorpt J Int Adsorpt Soc 2010;16(6):587e98. [20] Banu A, Friedrich D, Brandani S, Du¨ ren T. A multiscale study of MOFs as adsorbents in H2 PSA purification. Ind Eng Chem Res 2013; 52(29):9946e57. [21] Yang J, Lee CH. Adsorption dynamics of a layered bed PSA for H2 recovery from coke oven gas. AIChE J 1998;44(6):1325e34. [22] Ahn S, You YW, Kim KH, Lee CH. Layered two- and four-bed PSA process for H2 recovery from coal gas. Chem Eng Sci 2012;68(1):413e23. [23] Lee CH, Yang J, Ahn H. Effect of carbon-to-zeolite ratio on layered bed H2 PSA for coke oven gas. AIChE J 1999;45(3):535e45. [24] Jee J, Kim M, Lee C. Adsorption characteristics of hydrogen mixtures in a layered bed: binary, ternary, and five-component mixtures. Ind Eng Chem Res 2001; 40(3):868e76. [25] Sircar S, Golden TC. Purification of hydrogen by pressure swing adsorption. Sep Sci Technol 2000; 35:667–87. [26] Ritter, J.A., Yang, R.T., 1987.Equilibrium adsorption of multicomponent gas mixtures at elevated pressures. Industrial and Engineering Chemistry Research 26, 1679-1686. [27] Santos, J. C. Study of New Adsorbents and Operation Cycles for Medical PSA Units. Ph.D. Thesis, Faculty of Engineering of the University of Porto, Porto, Portugal, 2005. [28] Campo, M. C.; Magalhães, F. D.; Mendes, A. Comparative Study Between a CMS Membrane and a CMS Adsorbent: Part I - Morphology, Adsorption Equilibrium and Kinetics. Journal of Membrane Science.2010,346 (1), 15-25. [29] Santos, J. C.; Magalhaes, F. D.; Mendes, A. Contamination of Zeolites Used in Oxygen Production by PSA: Effects of Water and Carbon Dioxide. Ind. Eng. Chem. Res. 2008, 47 (16), 6197–6203. [30] Ferreira. D, Magalhaes.R, Taveira.P and Mendes, A. Effective Adsorption Equilibrium Isotherms and Breakthroughs of Water Vapor and Carbon Dioxide on Different Adsorbents. Ind. Eng. Chem.Res.2011, 10201-1021. 24

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[31] Ahn H, Yang J, Lee C-H. Effects of feed composition of coke oven gas on alayered bed H2 PSA process. Adsorption 2001; 7:339–56. [32] 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; 68:41323. [33] Santos MP, Grande CA, Rodrigues AE. Dynamic study of the pressure swing adsorption process for biogas upgrading and its responses to feed disturbances. Ind Eng Chem Res 2013; 52:5445–54. [34] Ruthven DM. Principles of adsorption and adsorption processes. New York: John Wiley and Sons; 1984. p. 209. [35] Kapteijn F, Moulijn J, Krishna R. The generalized Maxwell-Stefan model for diffusion in zeolites: sorbate molecules with different saturation loadings. Chem Eng Sci 2000; 55:2923–30. [36] Mathias PM, Kumar R, Moyer JD, Schork JM, Srinivasan SR, Auvil SR, et al. Correlation of multicomponent gas adsorption by the dual-site Langmuir model. Application to nitrogen/oxygen adsorption on 5A-zeolite. Ind Eng Chem Res 1996; 35:2477–83. [37] Dong-Kyu Moon, Dong-Geun Lee, Chang-Ha Lee H2 pressure swing adsorption for high pressure syngas from an integrated gasification combined cycle with a carbon capture process. Applied Energy 183 (2016) 760–774. [38] Ahn H, Chun C, Park M, Ahn IS, Lee CH. Thermal effects on the breakthrough curve of a hydrogen ternary system at a fixed bed. Sep Sci Technol 2001; 36(10):2121e45. [39] Xiao JS, Li RP, Benard P, Chahine R. Heat and mass transfer model of multicomponent adsorption system for hydrogen purification. Int J Hydrogen Energy2015; 40(1):4794e803. [40] Wakao, N.; Kaguei, S., Heat and mass transfer in packed beds. Gordon and Breach, Science Publishers, Inc.: New York, 1982. [41] Yagi, S.; Kunii, D. Studies on heat transfer near wall surface in packed beds. AIChE J. 1960, 6, 97-104. [42] Dantas TL, Luna FMT, Silva IJ, Torres AEB, de Azevedo DC, Rodrigues AE, et al. Carbon dioxide–nitrogen separation through pressure swing adsorption. ChemEngJ 2011; 172:698–704. [43] Sereno C, Rodrigues A. Can steady-state momentum equations be used inmodelling pressurization of adsorption beds? Gas Sep Purif 1993; 7:167–74. [44] Mauro Luberti, Daniel Friedrich, Stefano Brandani , HyungwoongAhn,Design of a H2 PSA for cogeneration of ultrapure hydrogen and power at an advanced integrated gasification combined cycle with pre-combustion capture. Adsorption 2014 20:511–524. [45] AspenTech, AspenAdsorptionV8.0guide. 2012.

25

List of tables Table 1 - Characteristics of the adsorption bed. Table 2 - Cyclic sequences of four-bed PSA process.

IP T

Table 3 - Dual-site Langmuir parameters, heats of adsorption and linear driving force parameters of gases on a commercial activated carbon.

N

U

SC R

Table 4 - Operating conditions.

M

A

List of figures

Fig. 1- schematic description of the system for producing electricity from bio-ethanol.

TE D

Fig. 2 - Experimental setup for measurement of breakthrough curves. Fig.3 - The pilot PSA system.

Fig. 4 - Schematic diagram of the cycles sequences used in the PSA simulation. Fig. 5- The effect of number of nodes on the concentration profile of methane.

EP

Fig.6-The effect of number of nodes on the temperature profiles in the top and the bottom of the column.

CC

Fig.7-Fitting of Langmuir-Freundlich and dual site Langmuir models to the experimental adsorption isotherm of CO at three temperatures.

A

Fig. 8 -CH4 isotherms at different temperatures on commercial activated carbon. Fig. 9 - CO isotherms at different temperatures on commercial activated carbon. Fig. 10 - CO2 isotherms at different temperatures on commercial activated carbon. Fig. 11 - H2 isotherms at different temperatures on commercial activated carbon. Fig. 12- Comparison between the simulation results and the experimental breakthrough data. 26

Fig. 13 - Temperature evolution with time at the top and the bottom of the column. Fig. 14 - Pressure history of four-column for one cycle. Fig. 15 - Feed flow rate profiles for four cycles after the steady state. Fig. 16 - Purge flow rate profiles for four cycles. Fig. 17 - Production flow rate for one and four cycles. Fig. 18 - Effect of adsorption pressure (a) and (b), P/F ratio (c) and (d), Production time

A

CC

EP

TE D

M

A

N

U

SC R

IP T

(e) and (f) and Production flow rate (g) and (h) on the hydrogen purity and recovery.

27

Adsorption bed

34

SC R

Bed length, L [cm]

Inside radius, ri[cm]

3.2

N

U

Outside radius, ro, cm

TE D

M

Heat capacity of wall, Cpw [J/kg K]

A

Material of wall [-]

3.5

Stainless steel

502

Inter- particle voidage

0.385

Intra-particle voidage

0.521

Thermal conductivity of the wall, Kw [J/m K s]

16.3

A

CC

EP

IP T

Table 1- Characteristics of the adsorption bed.

28

1

Bed 1

AD

Bed 2

SPPE

IDLE

PR

AD

Bed 3

BD

PG

FPPE

SPPE

IDLE

PR

Bed 4

FDPE

PP

SDPE

BD

PG

FPPE

97

1.5

1.5

97

4

5

6

7

8

FDPE

PP

SDPE

BD

11

12

FPPE

SPPE

IDLE

PR

PP

SPPE

BD

PG

FPPE

FDPE

PP

SDPE

97

1.5

N

AD

A

1.5

10

PG

U

FDPE

9

SPPE

IDLE

PR

AD

1.5

97

1.5

1.5

A

CC

EP

TE D

Time(s) 1.5

3

M

2

SC R

Step

IP T

Table 2- Cyclic sequences of four-bed PSA process.

29

IP T

Table 3- Dual-site Langmuir parameters, heats of adsorption and linear driving force

2.17.(10-7)

1.93.(10-7)

3620

2160

8.71.(10-5)

1.12(10-6)

1.64.(10-4)

2.17.(10-7)

3.60.(10-7)

2.92.(10-8)

1.93.(10-7)

9.24.(10-5)

7.96.(10-4)

2.0.(10-5)

1.64.(10-4)

0.3

0.5

0.3

0.7

10494

19678.76

30100

17968

EP

K5[1/bar]

k [ 1/s]

A

∆Hads[J/mol]

2370

A

9.24.(10-5)

TE D

K4[mol/kg.bar]]

CO

6.54.(10-9)

M

K3[1/bar]

CC

2.90.(10-7)

1260

K2[K]

CO2

N

K1[ mol/kg.bar]

CH4

U

H2

SC R

parameters of gases on a commercial activated carbon.

30

IP T

Table 4- Operating conditions.

SC R

Operating conditions

Adsorption pressure, [bar]

8.5

N

U

Desorption pressure, [bar]

M

Feed flow rate,[ L/min]

A

Feed temperature,[ °C]

1

24.6 2

1.37

0.2

Production flow rate,[ L/min]

0.75

A

CC

EP

TE D

Purge flow rate ,[ L/min]

31

IP T SC R

A

CC

EP

TE D

M

A

N

U

Fig. 1- schematic description of the system for producing electricity from bio-ethanol.

32

IP T SC R U N A M TE D EP CC A Fig. 2- Experimental setup for measurement of breakthrough curves. 33

IP T SC R U N A M TE D EP CC A Fig. 3- The pilot PSA system. 34

AD

FDPE

IP T

PP

BD

A

CC

EP

TE D

M

A

N

U

SC R

SDPE

PG

FPPE

SPPE

IDLE

PR

Fig. 4- Schematic diagram of the cycles sequences used in the PSA simulation. 35

IP T SC R U

A

CC

EP

TE D

M

A

N

Fig. 5- The effect of number of nodes on the concentration profile of methane

Fig.6-The effect of number of nodes on the temperature profiles in the top and the bottom of the column .

36

IP T SC R U N A M TE D EP CC A Fig.7 - Fitting of Langmuir-Freundlich and dual site Langmuir models to the experimental adsorption isotherm of CO at three temperatures. 37

IP T SC R U N A M TE D EP CC A Fig. 8- CH4 isotherms at different temperatures on commercial activated carbon. 38

IP T SC R U N A M TE D EP CC A

Fig. 9 - CO isotherms at different temperatures on commercial activated carbon.

39

IP T SC R U N A M TE D EP CC A

Fig. 10- CO2 isotherms at different temperatures on commercial activated carbon.

40

IP T SC R U N A M TE D EP CC A

Fig. 11- H2 isotherms at different temperatures on commercial activated carbon.

41

IP T SC R U N A M TE D EP CC A Fig. 12- Comparison between the simulation results and the experimental breakthrough data. The symbols represent the experimental data; the solid lines represent the results obtained by simulation. 42

IP T SC R U N A M TE D EP CC A Fig. 13- Temperature evolution with time at the top and the bottom of the column. The symbols represent the experimental data; the solid lines represent the results obtained by simulation. 43

IP T SC R U N A M TE D EP CC A Fig. 14- Pressure history of four-column for one cycle. The symbols represent the experimental data; the solid lines represent the results obtained by simulation. 44

IP T SC R U N A M TE D EP CC A Fig.15 - Feed flow rate profiles for four cycles after the steady state. The symbols represent the experimental data; the solid lines represent the results obtained by simulation. 45

IP T SC R U N A M TE D EP CC A Fig. 16- Purge flow rate profiles bedfour cycles. The symbols represent the experimental data; the solid lines represent the results obtained by simulation. 46

47

EP

CC

A TE D

IP T

SC R

U

N

A

M

IP T SC R U N A M TE D EP CC A Fig.17-Production flow rate for one (a) and four cycles (b). The symbols represent the experimental data; the solid lines represent the results obtained by simulation. 48

49

EP

CC

A TE D

IP T

SC R

U

N

A

M

50

EP

CC

A TE D

IP T

SC R

U

N

A

M

51

EP

CC

A TE D

IP T

SC R

U

N

A

M

52

EP

CC

A TE D

IP T

SC R

U

N

A

M

IP T SC R U N

Fig. 18 - Effect of adsorption pressure (a) and (b), P/F ratio (c) and (d), Production flow

A

rate (e) and (f) and Production time (g) and (h) on the hydrogen purity, recovery and

A

CC

EP

TE D

M

CO concentration.

53