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Record W1970971575 · doi:10.1002/cjce.22207

Life cycle analyses of bulk‐scale solid oxide fuel cell power plants and comparisons to the natural gas combined cycle

2015· article· en· W1970971575 on OpenAlexaffvenue
Jake Nease, Thomas A. Adams

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsLife-cycle assessmentCombined cycleNatural gasProcess engineeringEnvironmental scienceElectricity generationPower stationEngineeringPower (physics)Waste managementMechanical engineeringGas turbinesElectrical engineeringProduction (economics)ThermodynamicsEconomicsPhysics

Abstract

fetched live from OpenAlex

Abstract In this work, detailed cradle‐to‐grave life cycle analyses are performed for a current state‐of‐the art natural gas combined cycle and a bulk‐scale solid fuel cell power plant fuelled by natural gas. Life cycle inventories are performed for multiple configurations of each plant, including designs with carbon capture capability. Consistent boundaries (including all supply chain and upstream processes) and unit bases for each process are defined for each process. The ReCiPe 2008 life cycle assessment method is used to quantify the impacts of each plant at both mid‐ and end‐point levels. Three impact assessment perspectives (individualist, hierarchist, and egalitarian) are considered. The results of these life cycle analyses are compared in order to determine the environmental trade‐offs between potential power generation pathways. Results indicate that power generation using solid oxide fuel cells has a smaller life cycle impact than the natural gas combined cycle when the entire life cycle of each option is considered.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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

Citations20
Published2015
Admission routes2
Has abstractyes

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