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Record W4250346595 · doi:10.1149/ma2017-03/1/189

Fuel Composition in Pressurized SOFCs

2017· article· en· W4250346595 on OpenAlexaboutno aff
Aki Muramoto, Yuhdai Kikuchi, Yuya Tachikawa, Yusuke Shiratori, Shunsuke Taniguchi, Kazunari Sasaki

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMole fractionHydrogenEquilibrium constantChemical equilibriumCarbon fibersGas compositionThermodynamicsThermodynamic equilibriumPartial pressureAnodeChemistryMaterials scienceOxygenPhysical chemistryOrganic chemistryPhysics

Abstract

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Introduction Pressurized SOFC system combined with gas and steam turbine will achieve high power generation efficiency approaching 70% as the triple combined cycle power generation system. It has been reported that operating SOFCs under high pressure can make cell power output higher (1,2) . However, there are only a limited number of studies available on the fuel composition for pressurized SOFCs. In general, fuel gas consists of carbon, hydrogen and oxygen, so that the C-H-O equilibrium composition determines anode performance. In this study, fuel gas composition on the anode side of pressurized SOCFs was derived by thermochemical equilibrium calculation for considering various operating conditions in terms of carbon deposition and each gas molar fraction affecting power generation characteristics, all described in the C-H-O diagrams. Calculation Procedure We calculated carbon deposition region and each gas molar fraction under various equilibrium conditions upon suppling fuels composed of carbon, hydrogen and oxygen. Thermochemical calculations were carried out using three approaches: (i) calculation with equilibrium constants, using (ii) HSC Chemistry (Version 9.0.5, Outotec Research Oy, Finland) and (iii) FactSage (Version 6.3.1, Thermfact Ltd., Canada). As the first approach, we defined main reactions within the fuel and solved the system of equations using relational expression of equilibrium constants and elements ratios by Mathematica (Version 10.4.1, Hulinks Inc., Japan). HSC Chemistry and FactSage are both thermochemical equilibrium calculation software based on Gibbs free energy minimization, containing various databases. It has been known that the major constituents of equilibrium products are H 2 (g), H 2 O(g), CO(g), CO 2 (g), CH 4 (g), and C(s) (graphite) (3) . The line specifying the carbon deposition region means the equilibrium concentration of C(s) equal to 10 -6 of the initial carbon content in the fuel. The C-H-O diagrams are described in such a way between 100 and 1000 o C, and between 1 and 30 bar. Results and discussion Comparing Fig. 1 with Fig. 2, carbon deposition region boundaries vary with respect to temperature and pressure. The C-H-O diagrams clearly show that carbon deposition region expands on the oxygen-rich side and contracts on the hydrogen-rich side with decreasing temperature and/or increasing total pressure. It can be explained by the chemical equilibrium reactions (I) and (II): CH 4 ↔C(s)+2H 2 (I) 2CO↔C(s)+CO 2 (II) Reaction (I) is an endothermal reaction; on the hydrogen-rich side, equilibrium reaction (I) shifts to the left side, preventing carbon deposition at lower temperatures. With increasing total pressure, the equilibrium also shifts to the left side due to the decrease in the number of molecules. In the same way, Reaction (II) is an exothermal reaction and the number of molecules is lower on the right side, so that the equilibrium reaction shifts to the right side to promote carbon deposition. The C-H-O diagrams of various gases and the corresponding theoretical open circuit voltage are also calculated. These results suggest, for example, a decrease in the fraction of H 2 and CO under higher total pressure. References 1. S. C. Singhal, Solid State Ionics , 135 (1-4), 305-313 (2000). 2. Y. Kobayashi et al, ECS Trans. , 51 (1), 79-86 (2013). 3. K. Sasaki and Y. Teraoka, J. Electrochem. Soc. , 150 (7), A885-A888 (2003). Figure 1

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.627
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.225
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2017
Admission routes1
Has abstractyes

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