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Record W1991394617 · doi:10.1115/icef2012-92090

Achieving Low Emissions From a Biogas Fueled SI Engine Using a Catalytic Converter

2012· article· en· W1991394617 on OpenAlexafffund
Mark Tadrous, James S. Wallace

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiogasMethaneEnvironmental scienceWaste managementRenewable energyGreenhouse gasRenewable natural gasSpark-ignition engineElectricityFuel gasAutomotive engineeringEngineeringInternal combustion engineCombustionChemistryElectrical engineering

Abstract

fetched live from OpenAlex

Utilization of biogas is attractive from a greenhouse gas standpoint since it is carbon neutral due to the use of renewable resources. One source of biogas is anaerobic digestors. The biogas produced could be used to power IC engine-generator sets to produce electric power and heat on farms and in rural and northern communities. Use of local energy sources is particularly attractive in remote regions where liquid fuels must be shipped in via difficult terrain. Whatever the fuel, the engine must meet stringent exhaust emission standards. Biogas is typically used in spark ignition engines, where stoichiometric engine operation coupled to a three-way catalyst is a proven technology for achieving low emissions. An appropriate three-way catalyst was selected on the basis of tests with natural gas. A flow mixing system was used to create simulated biogas mixtures consisting of varying concentrations of methane, carbon dioxide, hydrogen and nitrogen. The effectiveness of the catalyst in achieving low emissions when the engine was fueled by the various simulated biogas mixtures was assessed.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.610
Threshold uncertainty score0.837

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 head0.020
GPT teacher head0.251
Teacher spread0.231 · 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

Citations1
Published2012
Admission routes2
Has abstractyes

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