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Record W2084009263 · doi:10.1021/ef800778g

Efficiency and Emissions Measurement of a Stirling-Engine-Based Residential Microcogeneration System Run on Diesel and Biodiesel

2009· article· en· W2084009263 on OpenAlexaff
Amir A. Aliabadi, Murray J. Thomson, James S. Wallace, Tommy Tzanetakis, Warren G. Lamont, Joseph Di Carlo

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldEngineering
TopicThermodynamic and Exergetic Analyses of Power and Cooling Systems
Canadian institutionsUniversity of Toronto
FundersNational Renewable Energy Laboratory
KeywordsBiodieselDiesel fuelEnvironmental scienceWaste managementDiesel engineBiofuelNOxCombustionWinter diesel fuelThermal efficiencyChemistryEngineeringAutomotive engineeringDiesel cycleCombustion chamber

Abstract

fetched live from OpenAlex

Concern with climate change and energy security has generated interest in both cogeneration and biofuels. This experimental study examines the performance of a residential microcogeneration system based on a Stirling engine fueled by diesel and biodiesel. Run on diesel, the system achieves a power efficiency of 11.7% and a heat efficiency of 78.7%. The corresponding efficiencies for the system, when run on biodiesel, are slightly lower at 11.5% and 77.5%, respectively. Particulate emissions for biodiesel are 69.2 mg/kWh, an order of magnitude higher than that of diesel (2.3 mg/kWh). The total unburned hydrocarbon emissions for biodiesel are higher than those of diesel. Emissions of carbon monoxide, nitrogen dioxide, methane, acetylene, ethylene, formaldehyde, and acetaldehyde are comparable between diesel and biodiesel, but nitric oxide emissions for diesel are observed to be higher at 151 ppm compared to those for biodiesel (117 ppm). The difference in the performance of the system is generally attributed to higher boiling range compounds in biodiesel that affect the flame stability, the fuel evaporation, and the complete burnout of the fuel. The system achieves energy efficiencies comparable to those of internal-combustion-engine-based and fuel-cell-based cogeneration systems.

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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.406
Threshold uncertainty score0.548

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 head0.008
GPT teacher head0.198
Teacher spread0.190 · 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".

Quick stats

Citations38
Published2009
Admission routes1
Has abstractyes

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