computational model for a high temperature …

2015 International Nuclear Atlantic Conference - INAC 2015

São Paulo, SP, Brazil, October 4-9, 2015 ASSOCIAÇÃO BRASILEIRA DE ENERGIA NUCLEAR - ABEN

ISBN: 978-85-99141-06-9

COMPUTATIONAL MODEL FOR A HIGH TEMPERATURE

ELECTROLYZER COUPLED TO A HTTR FOR EFFICIENT NUCLEAR

HYDROGEN PRODUCTION

Daniel González 1, Leorlen Rojas 1 , Jesús Rosales1 , Landy Castro1 , Abel Gámez1 ,

Carlos Brayner1 , Lázaro García 2 , Carlos García 2 , Raciel de la Torre 2 and Danny

Sánchez 3

1 Departamento de Energia Nuclear – Universidade Federal de Pernambuco

Ave. Prof. Luiz Freire, 1000

50740-420 Recife, PE

[email protected]

2 Instituto Superior de Tecnologías y Ciencias Aplicadas (InSTEC/ Cuba)

Av. Salvador Allende and Luaces

La Habana, Cuba

[email protected]

3 Universidade Estadual de Santa Cruz, Campus Soane Nazaré de Andrade,

Rodovia Jorge Amado, Km 16, Bairro Salobrinho

45662-900, Ilhéus, BA, Brasil

ABSTRACT

High temperature electrolysis process coupled to a very high temperature reactor (VHTR) is one of the most

promising methods for hydrogen production using a nuclear reactor as the primary heat source. However there

are not references in the scientific publications of a test facility that allow to evaluate the efficiency of the process

and other physical parameters that has to be taken into consideration for its accurate application in the hydrogen

economy as a massive production method. For this lack of experimental facilities, mathematical models are one

of the most used tools to study this process and theirs flowsheets, in which the electrolyzer is the most important

component because of its complexity and importance in the process.

A computational fluid dynamic (CFD) model for the evaluation and optimization of the electrolyzer of a high

temperature electrolysis hydrogen production process flowsheet was developed using ANSYS FLUENT®.

Electrolyzer’s operational and design parameters will be optimized in order to obtain the maximum hydrogen

production and the higher efficiency in the module. This optimized model of the electrolyzer will be incorporated

to a chemical process simulation (CPS) code to study the overall high temperature flowsheet coupled to a high

temperature accelerator driven system (ADS) that offers advantages in the transmutation of the spent fuel.

Keywords: High temperature electrolysis, nuclear hydrogen, electrolyzer efficiency, computational

fluid dynamic.

1. INTRODUCTION

Hydrogen economy is one of the most innovative energy concepts since the big oil time in the

20´s [1]. It proposes the substitution of fossils fuels as the baseline of the global energy system

for another energy carrier, hydrogen [2]. The difference between a fuel and an energy carrier

is that the energy carrier needs to be produced since it is not in pure state in nature and the fuel

need only be extracted.

mailto:[email protected]

INAC 2015, São Paulo, SP, Brazil.

Hydrogen production currently relies heavily on fossils fuels, since 90% of production comes

from steam reforming or low parameters conventional electrolysis [3]. Nuclear energy is

considered by many scientists as the main primary energy source to be used for a large-scale

hydrogen production. Two processes have the greater expectations of development, high-

temperature electrolysis and thermochemical water splitting cycles [4].

The study of the high temperature nuclear energy for hydrogen production has been increased

in recent years, has been created multinational projects such as EUROPAIRS (End-User

Requirements for industrial Process heat Applications with Innovative nuclear Reactors

for Sustainable energy supply) with the participations of 27 countries under the auspices of

the European Community [5]. Most of very high temperature reactor projects have been

assessed for determinate their capability to be used in the large-scale hydrogen production [6]–

[9].

2. HIGH TEMPERATURE ELECTROLYSIS. TADSEA.

Solid Oxide Electrolyzers (SOEC) have been studied especially in experimental research

focused, searching resistant materials according to high temperature and oxidation-reduction

environments [10]–[12]. However experimental investigations of these electrochemical

devices, which requires materials with special characteristics, are extremely expensive. Besides

these studies are complicated due to the difficulty of conductive extensive parametric analysis

to evaluate deeply the performance of the SOEC in response to the change in operating

conditions and design parameters, which has influence on the devices efficiency [13].

Physical-mathematical models can supply the challenges presented by experimental studies

and complement them so that can obtain a better understanding of the physical phenomena

occurring in the high temperature electrolysis process. That´s why in recent years have been

focused efforts on developing models that reproduce the operating conditions of the SOEC to

have a tool to characterize them with high levels of details and with less costs and limitations

[10], [14]–[18].

High temperature electrolysis (Fig. 1) unlike of low parameters electrolysis, requires an

external source of high parameters heat (over 900 oC) besides electricity, in this case is

proposed a pebbled bed very high temperature nuclear system (TADSEA) [19], [20].

Figure 1: Flowsheet diagram for a HTE process coupled to TADSEA.


To assess the reliability of the TADSEA for hydrogen production by high temperature

electrolysis process is proposed a computational model using a chemical process simulation

software (CPS) Aspen HYSYS® [21]. However, the electrolyzer is not a standard component

of these chemical processes simulators [21], in addition to this the electrolyzer is the most vital

component in the HTE flowsheet, it is proposes to construct a detailed model of the SOEC

employing a CFD software (ANSYS FLUENT®) to optimized the SOEC and then incorporate

it to the CPS flowsheet as an add-on component.

3. HIGH TEMPERATURE SOLID OXIDE ELECTROLYZER.

High temperature electrolysis is a variant of the conventional one, in which rupture of the water

molecule is carried out on heated steam to 750oC-900oC reaching superior levels of efficiency

than low parameters electrolysis. The increased efficiency is due to the molar Gibbs energy of

the reaction (ΔG) decreases from 1,23 V at 25 oC to ~ 0,9 V at 900 oC [22].

Water splitting electrochemical reaction takes place in electrolytic cell or electrolyzer. A high

temperature solid oxide electrolytic cell is composed of an electrolyte, both electrodes (anode

and cathode) and two connectors [23]. Water is turned into steam, by an external heat source

(TADSEA) before entering the electrolyzer, this steam is supplied to the electrode where

decomposes into hydrogen and oxygen according to reaction 1:

𝐻2𝑂 + 2𝑒− → 𝐻2 + 𝑂2− (1)

Oxygen ions are recovered on the other electrode as molecular oxygen following the equation

2:

𝑂2− →1

2𝑂2 + 2𝑒− (2)

During operation of the SOEC voltage can be expressed by Nernst equations (3) and (4):

𝑉 = 𝐸 + 𝜂𝑎𝑐𝑡,𝑐 + 𝜂𝑎𝑐𝑡,𝑎 + 𝜂𝑜ℎ𝑚𝑖𝑐 (3)

𝐸 = 𝐸0 +𝑅𝑇

2𝐹𝑙𝑛 (

𝑃𝐻2𝐼 (𝑃𝑂2

𝐼 )0,5

𝑃𝐻2𝑂𝐼 ) (4)

Where E0, is the equilibrium potential, 𝜂𝑜ℎ𝑚𝑖𝑐 the ohmics losses, 𝜂𝑎𝑐𝑡,𝑎 y 𝜂𝑎𝑐𝑡,𝑐 overpotentials

losses at the electrodes, 𝑃𝐻2𝑂𝐼 partial pressure at the interface electrode-electrolyte, R (8,3145

J/molK) universal gas constant, F (96,485 C/mol) Faraday´s constant and T local temperature

[15].

3.1 CFD Model for HTE Electrolyzer.

For a detailed description of the physical phenomena occurring in the process of high-

temperature electrolysis is necessary to solve the governing equations of fluid dynamics as well

as expressions that define the electrochemistry of the problem. So the computational tool used

must be able to integrate, in an iterative process, the results of the fluid dynamic and

electrochemical calculations to obtain a full description of the problem [14].

3.1.1. Computational fluid dynamic model for the HTE electrolyzer. Equations.


Development of the computational model for the SOEC is performed using the fluid dynamic

software ANSYS FLUENT®, which in addition to solving the equations of mass and energy

transfer has an additional module which allows to simulate electrolysis and fuel cells [24].

Heat and mass transfer in a SOEC.

The governing equations of CFD software [25] that will be solved in the problem domain are:

- Continuity: 𝜕(𝜌𝑈)

𝜕𝑥+

𝜕(𝜌𝑉)

𝜕𝑦+

𝜕(𝜌𝑊)

𝜕𝑧= 𝑆𝑚 (5)

-Momentum: 𝜕𝑢

𝜕𝑡+ 𝑢

𝜕𝑢

𝜕𝑥+ 𝑣

𝜕𝑢

𝜕𝑦+ 𝑤

𝜕𝑢

𝜕𝑧= −

1

𝜌

𝜕𝑝

𝜕𝑥+ 𝑢

𝜕2𝑢

𝜕𝑥2 + 𝑢𝜕2𝑢

𝜕𝑦2 + 𝑢𝜕2𝑢

𝜕𝑧2 (6)

𝜕𝑢

𝜕𝑡+ 𝑢

𝜕𝑣

𝜕𝑥+ 𝑣

𝜕𝑣

𝜕𝑦+ 𝑤

𝜕𝑣

𝜕𝑧= −

1

𝜌

𝜕𝑝

𝜕𝑦+ 𝑣

𝜕2𝑣

𝜕𝑥2 + 𝑣𝜕2𝑣

𝜕𝑦2 + 𝑣𝜕2𝑣

𝜕𝑧2 (7)

𝜕𝑢

𝜕𝑡+ 𝑢

𝜕𝑤

𝜕𝑥+ 𝑣

𝜕𝑤

𝜕𝑦+ 𝑤

𝜕𝑤

𝜕𝑧= −

1

𝜌

𝜕𝑝

𝜕𝑧+ 𝑣

𝜕2𝑤

𝜕𝑥2 + 𝑣𝜕2𝑤

𝜕𝑦2 + 𝑣𝜕2𝑤

𝜕𝑧2 (8)

-Energy: 𝐷𝐸

𝐷𝑡=

𝜕(𝑢𝜎𝑥𝑥)

𝜕𝑥+

𝜕(𝑣𝜎𝑦𝑦)

𝜕𝑦+

𝜕(𝑤𝜎𝑧𝑧)

𝜕𝑧+

𝜕(𝑢𝜏𝑦𝑥)

𝜕𝑦+

𝜕(𝑢𝜏𝑧𝑥)

𝜕𝑧+

𝜕(𝑣𝜏𝑥𝑦)

𝜕𝑥+

𝜕(𝑣𝜏𝑧𝑦)

𝜕𝑧+

𝜕(𝑤𝜏𝑧𝑦)

𝜕𝑧+

𝜕(𝑤𝜏𝑥𝑧)

𝜕𝑥+ +

𝜕(𝑤𝜏𝑦𝑧)

𝜕𝑦−

𝜕𝑞𝑥

𝜕𝑥−

𝜕𝑞𝑦

𝜕𝑦−

𝜕𝑞𝑧

𝜕𝑧 (9)

These equations (5-9) will provide the fluid dynamics of the electrochemical cell, but no

description of electrochemical parameters, so should be solved other expressions that

characterize the electrochemistry of the models domain.

Electrochemistry in a SOEC.

FLUENT´s electrochemical model solve two Equations (10-11), one that takes into

consideration the electron transport through the conductor materials and other for the ions

transport trough the electrolyte [24]:

∇(𝜎𝑠𝑜𝑙∇𝜙𝑠𝑜𝑙) + 𝑅𝑠𝑜𝑙 = 0 (10)

∇(𝜎𝑚𝑒𝑚∇𝜙𝑚𝑒𝑚) + 𝑅𝑚𝑒𝑚 = 0 (11)

Consumption ratios and total production of chemical species are calculated as:

𝑆𝐻2=

𝑀𝑤,𝐻2

2𝐹 𝐼 (12)

𝑆𝑂2=

𝑀𝑤,𝑂2

4𝐹 𝐼 (13)

𝑆𝐻2𝑂 = −𝑀𝑤,𝐻2𝑂

2𝐹 𝐼 (14)

Voltage and current density are calculated by the code using the Nernst equation, variables that

characterize the device operation. Also, it uses the Butler-Volmer expression (Eq. 15 and 16)

for calculate the lost potential due to lower reaction rate at the electrodes [26]:

𝑖 = 𝑖0 [𝑒𝑥𝑝 (𝛼𝑐𝜂𝑎𝑐𝑡𝐹

𝑅𝑇) − 𝑒𝑥𝑝 (−

𝛼𝑐𝜂𝑎𝑐𝑡𝐹

𝑅𝑇)] (15)

𝑖0 = 𝑎𝑖0,𝑟𝑒𝑓(𝑌𝑗)ϒ (16)

Integrating these equations to the governing equations of CFD the code will return a detailed

description of all phenomena that occur during high-temperature electrolysis in the


computational domain, resulting in a powerful tool to evaluate the influence of several

operating and design parameters on the SOEC´s efficiency.

3.2 Computational Domain for SOEC Using ANSYS FLUENT.

Several geometries are possible for SOEC electrolyzers, however planar configurations are the

most studied, a typical geometry of planar SOEC is shown in Figure 2:

Figure 2: Typical planar crossflow SOEC configuration.

Planar configuration may change considering variations in the shape of the flow channels and

gas flow direction. In this work, it is considered a planar crossflow geometry with the

dimensions that are summarized in Figure 3:

Figure 3: Main dimensions of the SOEC electrolyzer simulated.

Several materials are being studied for the production of SOEC [11], [27]–[29], for the

development of this model are used the materials listed in Table 1.

Table 1: SOEC materials and properties.

Part Anode Cathode Electrolyte Current Collectors Catalyst

Material Ni-YSZa LSMb YSZc Stainless-steel Ni-YSZ

Density (kg/m3) 6500 5620 5480 3000 6500

CP (J/kgK) 500 450 500 500 500

Thermal Cond (W/mK) 13,1 9,6 2,16 27 13,1

Porosity 0,37 0,375 - 0 0,37

Electrical Cond (1/ohm m) 112919 7045 2 850000 112919 aNi-YSZ: Nickel oxide+yttria-stabilized zirconia. bLSM: Strontium-lanthanum-manganite. cYSZ: Yttria-stabilized zirconia


3.3 Volume Discretization and Boundary Conditions.

In Figure 4 is shown the computational domain of the SOEC discretized and meshed to solve

the mathematical equations of HTE process using the finite volume method. Whereas the

geometry is planar and has no appreciable complexity is used a hexahedral elements meshing

method with an orthogonal quality 0,999 [30]. Due to the small dimensions of the electrodes

catalyst layers it is necessary to use a small minimum element size in these areas to have at

least two elements in each zone to avoid numerical converge problems [31].

Figure 4: SOEC mesh properties.

Electrolyzers operating parameters and input conditions were obtained from literature and are

summarized in Table 2 [32], [33].

Table 2: Parameters used in the 3D CFD modelling analysis of a planar SOEC.

Q (kg/s) V (m/s) T (K) H2 H2O O2 N2

Anode inlet 5.00E-06 0.2638 1103 0 0.6 0 0.4

Cathode inlet 5.00E-06 0.193 1103 0 0 0.2 0.8

Operating Temperature (K) 1103 Operating Voltage (V) 1.32

Electrode Porosity 0.37 Open Circuit Voltage (V) 0.82

Because some of these parameters as the speed of inlet gas, its composition, the porosity of the

electrodes among others, are relevant to the model optimization are just shown the initial

values, as these will be varied in further analyzes.

4. RESULTS AND DISCUSSION.

For validation of the physical-mathematical model built in ANSYS FLUENT®, were

reproduced operating conditions of a numerical model for a SOEC published by Ni (2009)

using a two-dimensional formulation [15]. Operating and inputs conditions reported by [15] to

the proposed model were applied and the results obtained are shown in the Table 3:

Table 3: Comparison between some parameters of the SOEC model proposed by Ni

(2009) and the proposed model at the same operation conditions.

Ni (2009) Proposed Model

H2 Mass Fraction at Anode Outlet 0.373 0.375

Average SOEC Temperature at Outlet 1181.5 K 1181.9 K

Elements 141204

Nodes 149940

Quality 0,99919256


Although both models differ in several numerical aspects, such as numerical solving

methodology employed and geometrical aspects (the proposed model is 3D) there is a good

agreement for the H2 mass fraction at the anode outlet and the average cell temperature of both

models as can be observed in Table 3. Other hydrodynamic aspects such as velocity profiles

and flow lines also presented strong similarity. Considering that the results reported by [15]

were obtained from a numerical model and these, at the same time, were verified by

experimental parametric studies and compared with theoretical curves. The proposed model in

this work have a notable coincidence with the reported by [15], which represents a validation

criteria for the model developed.

4.1 Model Analysis.

Initial model shown in Table 2 will be analyzed hydrodynamic and electrochemically and then

perform an optimization of some operation and design parameters, in order to increase the

device efficiency. In Table 4 are shown the results of chemical species distribution,

thermodynamics and electrochemical parameters obtained for initial conditions, notice the

variation of H2 mass fraction at the anode outlet, where a value of 0.1442 is obtained. Likewise

a decrease in the mass fraction of H2O corresponding to water consumption in the process can

be observed.

Table 4: Results of the SOEC proposed model for initial conditions.

Q (kg/s) V (m/s) T (K) H2 H2O O2 N2

Anode Outlet 2.33E-06 0.3272 1145.41 0.1442 1.04E-06 0 0.8557

Cathode Outlet 7.67E-06 0.3524 1145.63 0 0 0.4784 0.5215

Cell Temperature (K) 1145.14 Current (A) 32.1987

Current Density (A/cm2) 0.504 Electric Potential (V) 1.26403

Contours of chemical species in the flow channels at the SOEC are also obtained, seeking that

there is a growing behavior of hydrogen mass fraction with the channel length, as expected

theoretically and it is reported by experimental studies and other HTE simulation studies

[15][34]–[36]. Figure 5 shows the mass fraction in both flow channels, anode and cathode, as

well as the mass fraction variation profiles along the flow direction.

(a) (b)


(c) (d)

Figure 5: Chemical species at the flows channels of the SOEC proposed model.

As observed in Figures 5 (a) and (b), the mass fractions of H2 and H2O vary linearly with the

channel length and homogeneously in the entire area of the electrolytic cell as reported in other

studies [15],[37], which has benefits for device durability for being more desirable

mechanically and economically [38]. In Figures 5 (c) and (d) are shown the variation of the

mass fraction of N2 and O2, whose behavior corresponds to expected, increasing amount of O2

due to water molecule dissociation and the diffusion of molecular oxygen through electrolyte,

which results in a decrease in the mole fraction of N2. Inhomogeneity in the diffusion of O2

through the electrolyte occurs due to the crossflow configuration channels. As observed in

Figure 5 (b), for initial conditions, the H2O feed into the electrolytic cell is consumed totally,

because by the 70 mm the mass fraction is almost zero.

4.2 Effect of the Operational Voltage.

Figure 6 presents a graph of cell voltage versus current density known as activation curve. To

obtain this graph the current density of the electrolytic cell is varied to obtain the operating

voltage of the cell, obtaining the characteristic voltage increase when increasing the current

density [39].

Figure 6: Activation curve for the initial conditions for the SOEC model.


The curve obtained besides having the behavior predicted by the theory, has a high coincidence

with the expected values and those reported by other authors in experimental work and

numerical simulations as works [10], [15], [40]. As expected, the variation of the operating

voltage of the electrolytic cell influences other operating parameters which in turn will

influence the production of H2 and device efficiency. In Figure 7 (a) the variation of the

chemical species with the operating voltage for the initial conditions is shown. As shown in

Figure 7 for 1.32 V mass fractions of H2 (0.1442) and O2 (0.4784) reach the maximum values

for the other initial conditions, for this reason this voltage will be assumed hereinafter as the

operating parameter of the SOEC.

Figure 7: Species mass fractions and cell temperature for different operational voltages

of the SOEC model proposed.

The variation of average temperature in SOEC with the operating voltage is shown in Figure

7, as reported by most authors, the temperature decrease to values of voltage near to the open

circuit voltage (OCV= 0.82 V) and then begin to grow according to Nernst equation [33], [40],

[41].

4.3 Effect of the Inlet Mass Flow Rates.

For the model with initial operating conditions the water mass fraction at anode outlet is near

to zero (2.42e-12 kg/s), even before the channels end, which influences the production of

hydrogen. For this reason was performed an analysis to evaluate the influence of the initial

water mass flow into the electrolyzer in other cells parameters. The study was performed

without varying the species mass fractions (H2O/N2= 0.6/0.4). Thus they were obtained the

results shown in Figure 8.

Figure 8: Species mass fractions and cell temperature for different inlet mass flows at

anode channel of the SOEC model proposed.


Increasing the amount of mixture H2O/N2 at anode inlet causes an increase in the amount of

hydrogen produced, as shown in Figure 8 (a). However the consumed water mass fraction

decrease from almost 1.0 in the case with 5.0e-6 kg/s flow to 0.2 in the case of 1.0e-5kg/s. This

effect can be explained because of the decrease in the average cell temperature (Figure 8 (b))

and increased velocity and turbulence in the flow channels, which is unfavorable to the

occurrence of the electrochemical reaction [15], [40].

4.4 Effect of the Anode Gas Composition.

Inlet gas composition has a marked influence on the SOEC electrochemistry, because affects

important parameters such as current density and area specific resistance (ASR). To evaluate

this was conducted a parametric analysis varying the inlet gas composition from baseline

obtaining the results shown in Figure 9.

(a) (b)

(c) (d)

Figure 9: Influence of the composition at the anode mass flow inlet in the SOEC model

proposed. (a) Current density. (b) ASR. (c) Components mass flows. (d) Temperature.

Increasing the mass fraction of water in the inlet flow to the electrolyzer, it produced an

increase in the hydrogen mass fraction obtained as shows the Figure 9 (c). The effect of

increasing water mass fraction also results in a decrease of the area specific resistance (ASR),

thereby decrease the average cell temperature and the current density. These two effects have

opposite influence on the hydrogen production, because decreasing the operating temperature

is unfavorable, while increasing the current density it is not, however better overall

performance and increase in the hydrogen mass flow is obtained. Because of this results, is


assumed as the nominal value of water mass fraction H2O/N2=0.9/0.1 due to the improvements

obtained in the hydrogen production.

4.5 Optimized Model of the SOEC.

Once analyzed the influence of several operating parameters were established as nominal the

most favorable for hydrogen production, which is the most important parameter for assess the

device efficiency. They were also considered other electrochemical parameters as the ASR and

the current density so the SOEC efficiency is maximized. A comparison between the initial

model and the optimized is shown in Table 5.

Table 5: Comparison between the initial model and the optimized model.

Anode

Outlet

Mass flow (kg/s) 2.75E-06 2.33E-06

Velocity (m/s) 0.4491 0.3272

Temperature (K) 1152.1598 1145.41

H2 0.1491 0.1442

H2O 6.32E-01 1.04E-06

O2 0 0

N2 0.2184 0.8557

Cathode

Outlet

Mass flow (kg/s) 9.25E-06 7.67E-06

Velocity (m/s) 0.4272 0.3524

Temperature (K) 1151.991 1145.636

H2 0 0

H2O 0 0

O2 0.4812 0.4784

N2 0.5187 0.5215

Average Temperature (K) 1151.26 1145.14

Current Density (A/cm2) 0.6135 0.5040

Current (A) 39.2395 32.1987

ASR (Ωcm2) 2.0557 2.5044

As shown in Table 5, the optimized SOEC model presents significant improvements in the

fraction of H2 produced due to improved electrochemical process parameters as the current

density and decreasing the ASR, which favors the speed of the electrochemical water splitting

reaction [17].

5. CONCLUSIONS.

A 3D model of a planar electrolytic cell was developed using CFD software. The results of the

simulation model were compared to data reported by other authors, presenting good agreement,

so that the model developed is a convenient tool for the study of these devices. Several

parametric studies to determine the optimum operating parameters of the proposed model were

conducted.


Contours of H2O and H2 in the anode channel presented a linear behavior, which vary because

of the electrochemical reaction in this part of the cell. For the initial model H2O is consumed

to 70 mm channels length, which decreases the efficiency of the cell. The contours of chemical

species obtained for N2 and O2 have a non-uniform behavior due to the diffusion of O2 anode

channel, it is affected by the direction of the channels (crossflow). For the initial model it was

obtained the activation curve which has a good agreement with data reported by others authors

using mathematical modeling and experimental studies. It was found that for 1.32 V as

operating voltage, the model shown the highest mass fraction of H2 produced, lower ASR

values and higher values of current density are obtained, whereby the speed of the

electrochemical reaction is favored and hence the electrolyzer efficiency. This value was

established as the operating parameter of the SOEC.

It was obtained that increasing the inlet mass flow channel has a slight increase in H2

production, then these values begin to decrease. This can be explained by the decrease of cells

temperature, increasing turbulence and ASR due to variation in the amount of H2O in the anode

channel inlet.

The influence of the composition of the inlet gases was determined by performing a parametric

study, obtaining an improvement in some electrochemical parameters such as current density

and ASR and a decrease in the average cell temperature which entails a slight increase in the

hydrogen produced.

Optimized model was analyzed and it shows huge improvements in the hydrogen mass fraction

produced and other electrochemical variables compared to the initial model. Results obtained

in this work allow a better understanding of physic-chemical phenomena of SOEC and it is a

useful tool for its design optimization. These will be used to build an additional component in

the chemical process simulation software to assess the reliability of TADSEA coupled to an

HTE flowsheet.

ACKNOWLEDGMENTS

The authors would like to acknowledge to CAPES (Coordenação de Aperfeiçoamento de

Pessoal de Nível Superior), to FACEPE (Fundação de Amparo à Ciência e Tecnologia de

Pernambuco) and PROTEN (Programa de Pós-Graduação em Tecnologias Energéticas e

Nucleares) from UFPE (Universidade Federal de Pernambuco) for their support to the

researchers.

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