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Record W4309797168 · doi:10.1115/icef2022-90473

Combustion and Emission Performance of a Syngas-Diesel Dual-Fuel Generator

2022· article· en· W4309797168 on OpenAlexaffabout
Ayşegül Sağlam Arslan, Shouvik Dev, Amin Yousefi, David Stevenson, Brian Liko, James W. Butler, Hongsheng Guo, Madjid Birouk

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of ManitobaNational Research Council Canada
Fundersnot available
KeywordsSyngasDiesel fuelInlet manifoldDiesel engineEnvironmental scienceCombustionDiesel cycleAutomotive engineeringTurbochargerInternal combustion engineWaste managementEngineeringPetrol engineGas compressorChemistryHydrogenMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Remote or off-grid communities in Canada and across the world heavily rely on diesel-fueled generators to meet their electrical power and heating needs. Reducing diesel consumption of these generators with locally produced sustainable fuels such as syngas has the double benefit of reducing the greenhouse gas (GHG) emissions and the cost of transporting diesel to remote locations. In remote/off-grid areas, syngas can be produced from local biomass or waste through gasification or pyrolysis. The aim of this study was to investigate the combustion and emission performance of a syngas-diesel dual-fuel generator at a constant load condition under varying syngas flow rate and composition. This experimental study was carried out using a 30-kilowatt (kW) generator with a four-stroke, four-cylinder, turbo-charged, and electronically controlled direct injection diesel engine. The intake manifold of the engine was modified to introduce syngas upstream of the engine’s turbocharger. Syngas was simulated using individually controlled flow rates of carbon monoxide (CO), hydrogen (H2), carbon dioxide (CO2) and nitrogen (N2) from compressed gas cylinders before their mixing and introduction into the engine intake manifold. Two practical syngas compositions with varying CO/H2 ratios were evaluated. The engine was operated with a programmable engine control unit to control the diesel direct injection events. Electrical load on the generator was controlled by a load bank set to 11 kW (5.25 bar IMEP) and the engine speed was 1800 rpm. The experimental results revealed that increasing the syngas flow rate at a fixed diesel injection timing and duration caused the indicated thermal efficiency (ITE) of the engine to decrease. NOx emissions decreased with increasing syngas flow rate but CO, particulate matter (PM) and CO2 emissions increased. Increasing the CO/H2 ratio in the syngas caused the ITE to decrease and the CO and CO2 emissions to increase. The laminar flame speed (LFS) and ignition delay (ID) of syngas-air mixture were calculated to develop a further insight into the experimental results.

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: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.374

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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations6
Published2022
Admission routes2
Has abstractyes

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