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Record W2275727621

Chemical Kinetic Modeling of Biofuel Combustion

2010· dissertation· en· W2275727621 on OpenAlexfundno aff
S. Mani Sarathy

Bibliographic record

VenueTSpace · 2010
Typedissertation
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiofuelCombustionKinetic energyEnvironmental scienceWaste managementChemistryBiochemical engineeringChemical engineeringEngineeringPhysicsOrganic chemistryClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

Bioalcohols, such as bioethanol and biobutanol, are suitable replacements for gasoline, while biodiesel can replace petroleum diesel. Improving biofuel engine performance requires understanding its fundamental combustion properties and the pathways of combustion. This study's contribution is experimentally validated chemical kinetic combustion mechanisms for biobutanol and biodiesel. Fundamental combustion data and chemical kinetic mechanisms are presented and discussed to improve our understanding of biofuel combustion. \n \nThe net environmental impact of biobutanol (i.e., n-butanol) has not been studied extensively, so this study first assesses the sustainability of n-butanol derived from corn. The results indicate that technical advances in fuel production are required before commercializing biobutanol. The primary contribution of this research is new experimental data and a novel chemical kinetic mechanism for n-butanol combustion. The results indicate that under the given experimental conditions, n-butanol is consumed primarily via abstraction of hydrogen atoms to produce fuel radical molecules, which subsequently decompose to smaller hydrocarbon and oxygenated species. The hydroxyl moiety in n-butanol results in the direct production of the oxygenated species such as butanal, acetaldehyde, and formaldehyde. The formation of these compounds sequesters carbon from forming soot precursors, but they may introduce other adverse environmental and health effects. \n \nBiodiesel is a mixture of long chain fatty acid methyl esters derived from fats and oils. This research study presents high quality experimental data for one large fatty acid methyl ester, methyl decanoate, and models its combustion using an improved skeletal mechanism. The results indicate that methyl decanoate is consumed via abstraction of hydrogen atoms to produce fuel radicals, which ultimately lead to the production of alkenes. The ester moiety in methyl decanoate leads to the formation of low molecular weight oxygenated compounds such as carbon monoxide, formaldehyde, and ketene, thereby reducing the production of soot precursors. \n \nThe study concludes that the oxygenated molecules in biofuels follow similar combustion pathways to the hydrocarbons in petroleum fuels. The oxygenated moiety's ability to sequester carbon from forming soot precursors is highlighted. However, the direct formation of oxygenated hydrocarbons warrants further investigation into the environmental and health impacts of practical biofuel combustion 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 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)0.001
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: none
Teacher disagreement score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.002

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 head0.015
GPT teacher head0.295
Teacher spread0.280 · 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

Citations7
Published2010
Admission routes1
Has abstractyes

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