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Record W2017612547 · doi:10.1002/fuce.201200004

Exergoeconomic Analysis of a Combined Ammonia Based Solid Oxide Fuel Cell System

2012· article· en· W2017612547 on OpenAlexafffund
Ehsan Baniasadi, İbrahim Dinçer, G.F. Naterer

Bibliographic record

VenueFuel Cells · 2012
Typearticle
Languageen
FieldMaterials Science
TopicAdvancements in Solid Oxide Fuel Cells
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsExergySolid oxide fuel cellStack (abstract data type)Capital costHeat exchangerProcess engineeringGas compressorExergy efficiencyEnvironmental scienceMaterials scienceWaste managementComputer scienceEngineeringChemistryMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract An exergoeconomic study of an ammonia‐fed solid oxide fuel cell (SOFC) based combined system for transportation applications is presented in this paper. The relations between capital costs and thermodynamic losses for the system components are investigated. The exergoeconomic analysis includes the SOFC stack and system components, including the compressor, microturbine, pressure regulator, and heat exchangers. A parametric study is also conducted to investigate the system performance and costs of the components, depending on the operating temperature, exhaust temperature, and fuel utilization ratio. A parametric study is performed to show how the ratio of the thermodynamic loss rate to capital cost changes with operating parameters. For the devices and the overall system, some practical correlations are introduced to relate the capital cost and total exergy loss. The ratio of exergy consumption to capital cost is found to be strongly dependent on the current density and stack temperature, but less affected by the fuel utilization ratio.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.246
Teacher spread0.234 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

Quick stats

Citations5
Published2012
Admission routes2
Has abstractyes

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