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Transactions of the Korean Hydrogen and New Energy Society - Vol. 32 , No. 6

[ Article ]
Transactions of the Korean Hydrogen and New Energy Society - Vol. 32, No. 6, pp. 470-476
Abbreviation: KHNES
ISSN: 1738-7264 (Print) 2288-7407 (Online)
Print publication date 30 Dec 2021
Received 01 Dec 2021 Revised 16 Dec 2021 Accepted 20 Dec 2021

Parametric Study on High Power SOEC System
1Department of Clean Fuel and Power Generation, Korea Institute of Machinery & Materials (KIMM), 156 Gajeongbuk-ro, Yuseong-gu, Daejeon 34103, Korea
2Department of Environment and Energy Mechanical Engineering, University of Science and Technology (UST), 156 Gajeongbuk-ro, Yuseong-gu, Daejeon 34113, Korea

고출력 SOEC 시스템의 매개변수 연구
뚜안앵1, 2 ; 김영상1, 2, ; 잡반티엔1 ; 이동근1 ; 안국영1, 2
1한국기계연구원 청정연료발전연구실
2과학기술연합대학원대학교 환경에너지기계공학과
Correspondence to :

2021 The Korean Hydrogen and New Energy Society. All rights reserved.
Funding Information ▼


In the near future, with the urgent requirement of environmental protection, hydrogen based energy system is essential. However, at the present time, most of the hydrogen is produced by reforming, which still produces carbon dioxide. This study proposes a high-power electrolytic hydrogen production system based on solid oxide electrolysis cell with no harmful emissions to the environment. Besides that, the parametric study and optimization are also carried to examine the effect of individual parameter and their combination on system efficiency. The result shows that the increase in steam conversion rate and hydrogen molar fraction in incoming stream reduces system efficiency because of the fuel heater power increase. Besides, the higher Faraday efficiency does not always result a higher system efficiency.

Keywords: Solid oxide electrolysis cell, Parametric study, Faraday efficiency, Steam conversion rate
키워드: 고체 산화물 전해전지, 매개변수 연구, 파라데이 효율, 스팀 전환율

1. Introduction

Currently, the pressure on fossil fuel reduction is increasing year by year due to global warming and climate change. This scenario opens a very promising future for hydrogen production. Not only playing as a clean fuel, hydrogen is also an alternative energy storage chemical in which electricity can be converted into hydrogen to be stored. Most of the current hydrogen produced is by reforming technologies because these technologies are mature at the time and low production cost, between 1-2 dollars1). However, producing hydrogen by reforming also generates carbon dioxide as by product while the future hydrogen society definitely requires a less polluted or even zero-carbon emitted technology. Electrolysis hydrogen production has recently been the center of focus because it can effectively utilize the excess electricity generated from renewable sources such as wind energy, solar energy, etc. The most challenging barrier of electrolysis hydrogen production is its high cost, normally from 3 to 6 $/kg H2 in case of polymer exchange membrane electrolysis cell2), or 2.8 to 5.8 $/kg H2 in case of solid oxide electrolysis cell (SOEC)3,4), a more efficient cell. In order to reduce production cost, there are several ways. Among them, increasing stack power to produce more hydrogen with the same capital cost attracting attention from researchers1,5). This study introduces a high power SOEC system, then parametric study will be used to find optimal point.

2. High power SOEC system and its modeling by simulation
2.1 Description of analyzed system

Fig. 1 shows a flow diagram of the analyzed high power SOEC system. Liquid water is fed to pre-vaporizer where it is heated up by the high temperature flow out from fuel heat exchanger (F-HEX). After pre-vaporizer, hot water comes to vaporizer, receiving heat from heat source such as nuclear reactor, concentrated solar panel, etc. vaporizes into steam. This steam recuperates the heat from extremely hot flue gas stream out from SOEC at the F-HEX, before going through an electrical heater (F-heater) which increases the steam temperature to SOEC operating temperature. In the remaining side of SOEC, air is blown to an air heat exchanger (A-HEX) where it receives heat from the outgoing air stream from SOEC, then once again the air stream is heated up by an electrical heater (A-heater) before being supplied to SOEC. Through the electrochemical reaction of SOEC, the supplied electricity and steam are converted into oxygen and hydrogen. Then, mixes with the unconverted steam to be H2-rich hot stream, exiting SOEC to F-HEX. After transferring heat to cold steam at F-HEX and pre-vaporizer, H2-rich stream is split into two streams; one stream is recirculated to vaporizer to recover steam and also keep H2 molar fraction in inlet steam higher than certain value6). The remaining flows through condenser where steam condenses, and is removed from gaseous mixture which is finally separated to get pure hydrogen.

Fig. 1. 
Flow diagram of the high power SOEC system

2.2 Assumption used in the system simulation

Table 1 summarizes the assumptions used in system modeling and simulation7-11). The current density is assumed to be 1 A/cm2 higher than almost normal SOEC5,12,13). Beside, operating condition of cell is chosen at 715˚C and 1 atm. Furthermore, the 36 kW stack includes 380 cells. Other essential coefficients such as inverter efficiency, Faraday efficiency, heat loss are set at widely used values: 92%, 92%, and 5%, respectively10). Lastly, the heat supplied to SOEC system is assumed from a 300˚C waste steam stream with mass flow rate of 15 kg/h.

Table 1. 
Assumptions and parameters used in system simulations
Parameter Value Unit
SOEC stack
Operating pressure 1 atm
Temperature 7157) °C
Current density 18) A/cm2
Cell voltage 1.2859) V
Cell number 350 cells
Faraday efficiency 92 %
Heat loss 510) %
Input heat (external steam)
Temperature 30011) °C
Mass flow rate 15 kg/h

2.3 Genetic algorithm optimization

The analyzed cycle was modeled using the EBSILON® (Steag, Zwingenberg, Germany) Professional commercial software package14). Fig. 2 shows a screenshot of the EBSILON software used for the analyzed cycle. During optimization process genetic algorithm is employed to find optimal point. Though genetic algorithm does not guarantee the global optimal point, this result suggests a parameter set for further investigation or analysis.

Fig. 2. 
Screenshot of the EBSILON software used for the simulation

3. Performance analysis

To examine the performance of SOEC system, parametric analysis method is conducted with external steam temperature, heat exchanger effectiveness, steam conversion rate, hydrogen molar fraction, Faraday efficiency, and cell voltage. Table 2 shows the range of each variable in parametric study and optimization6,9,11,15). In each examination, only studied parameter varies, others are kept at design condition. On the other hand, in the final step all listed parameters with their ranges are brought into an optimization to find the optimal condition. In both parametric studies and optimization, system efficiency is measured for comparison. Eq. (1) describes how system efficiency is calculated.

  • where:
  • ηsys : system efficiency
  • m˙H2out : mass flow rate of produced hydrogen
  • LHVH2 : low heating value of hydrogen at 25˚C
Table 2. 
Variable range in parametric study and optimization
Variable name Unit Range
HEX eff - 0.7-0.9
Steam temperature ˚C 200-70011)
Steam conversion rate - 0.5-0.815)
H2 molar fraction (xH2) - 0.2-0.56)
Faraday efficiency - 0.92-0.97
Cell voltage V 1.285-1.3059)

4. Result and discussion
4.1 Effect of external steam temperature and heat exchanger effectiveness

The figures show the system efficiency as function of each parameter in parametric study. In Fig. 3, external steam temperature is proportional to the amount of heat fed to the system, so the system efficiency increases when steam temperature increases, as expected. In the same way, the HEX effectiveness also affects positively to the system efficiency because it performs the heat recuperation of heat exchanger as shown in Fig. 4. Moreover, the dependence of system efficiency to HEX effectiveness is very high; about 10 percent points increases in system efficiency when HEX effectiveness changes from 0.7 to 0.9.

Fig. 3. 
System efficiency depends on the external steam temperature

Fig. 4. 
System efficiency depends on the HEX effectiveness

4.2 Effect of steam conversion rate and hydrogen molar fraction

Fig. 5 shows the system dependence on steam conversion. When steam conversion rate increases, the H2 content in the output of SOEC stack is also higher, leading to the lower recirculation flow, and then less recovered heat from fuel off-gas. As a result, the fuel heater power increases, causing slightly decrease in system efficiency. In Fig. 6, the effect of hydrogen molar fraction is quite similar to that of steam conversion rate. A higher hydrogen molar fraction in stack inlet stream requires a smaller recirculation flow, leading to less recuperated heat amount. Consequently, the F-heater has to provide more heat to achieve deign temperature, reducing total system efficiency. However, the amplitude is bigger than that in Fig. 5 due to the higher recycle blower power consumption.

Fig. 5. 
System efficiency depends on the steam conversion

Fig. 6. 
System efficiency depends on the H2 molar fraction

4.3 Effect of Faraday efficiency and cell voltage

Fig. 7 shows the dependency of system efficiency on Faraday efficiency. The SOEC efficiency increases with Faraday efficiency. However, it affects the heat release from or absorption into SOEC stack, consequently the whole system efficiency does not always increase with Faraday efficiency’s increase. The maximum system efficiency is 89.7% when Faraday efficiency is 0.96. In Fig. 8, system efficiency decreases when cell voltage increases because SOEC works at low faraday efficiency (0.92) that is in exothermic condition. Therefore, the higher voltage makes the higher loss.

Fig. 7. 
System efficiency depends on faraday efficiency

Fig. 8. 
System efficiency depends on the cell voltage

4.4 Optimization

Because Faraday efficiency effect is not monotonous, it is set at discrete values from 0.92 to 0.97 in optimization. Besides, the cell voltage also chosen at 1.285 V where the stack is almost at thermal balance point, neither absorb nor release heat9). In addition, the parametric study in the previous section also pointed out the system efficiency is maximum with cell voltage value of 1.285 V.

Table 3 shows the optimization results with Faraday efficiency from 0.92 to 0.97. In all the cases, the optimal results are found at the boundary value of the external steam temperature, 700˚C. However, the result also shows that, though the effect of all parameters are monotonous, the optimal point is not always achieved when the examined variables at the boundary. For example, when Faraday efficiency is 0.97, optimal point is achieved at HEX effectiveness of 0.787, steam conversion rate of 0.57 and hydrogen molar fraction of 0.200. This reveals the combination and correlation effect between the parameters.

Table 3. 
Optimization result
Faraday efficiency Cell voltage HEX
External steam
Steam conversion
H2 molar
fraction inlet
0.92 1.285 0.9 700 0.58 0.280 87.167
0.93 0.9 700 0.68 0.251 88.833
0.94 0.9 700 0.58 0.207 91.382
0.95 0.9 700 0.75 0.285 91.437
0.96 0.9 700 0.58 0.201 92.133
0.97 0.787 700 0.57 0.200 92.434

Though genetic algorithm does not guarantee the global optimal point, this result suggests a parameter set for further investigation or analysis.

5. Conclusions

This study proposes a high power SOEC system and then, examined the effect each parameter including external steam temperature, heat exchanger effectiveness, steam conversion rate, hydrogen molar fraction, Faraday efficiency, and cell voltage to the system efficiency. Finally, the optimization based on genetic algorithm was conducted to see the combining effect of all listed parameters on system performance. In conclusion, there are several points can be summarized as following:

Higher steam conversion rate leads to high concentration of H2 in the recirculation flow, hence the fuel heater power increases. As a result, the system efficiency drops as fuel utilization increases.

Faraday efficiency increase can make SOEC changes from exothermic to endothermic condition, thus the system efficiency trend and the optimal points of the operation changed too.

The genetic optimization method showed the optimal point in which endothermic operation is better option.

The concentration of H2 in the feed fuel, steam conversion rate, and Faraday efficiency should be simultaneously considered in optimizing system operation.


This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20213030040110).

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