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Record W3164660916 · doi:10.31603/ae.3692

Performance and Fuel Consumption of Diesel Engine Fueled by Diesel Fuel and Waste Plastic Oil Blends: An Experimental Investigation

2020· article· en· W3164660916 on OpenAlexaff
Sunaryo Sunaryo, Priyo Adi Sesotyo, Eqwar Saputra, Agus P. Sasmito

Bibliographic record

VenueAutomotive Experiences · 2020
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsDiesel fuelThrust specific fuel consumptionWaste managementDiesel engineCarbureted compression ignition model engineWinter diesel fuelEnvironmental sciencePyrolysis oilFuel oilThermal efficiencyFuel efficiencyLow-density polyethylenePyrolysisPulp and paper industryMaterials scienceAutomotive engineeringDiesel cycleEngineeringPolyethyleneInternal combustion engineCompression ratioChemistryComposite materialCombustion

Abstract

fetched live from OpenAlex

This study analyzes the performance of the diesel engine in terms of power, torque, specific fuel consumption, and thermal efficiency using diesel and pyrolysis oil. The waste plastic oil (WPO) used in this research was produced through a pyrolysis process using raw materials from Low-density Polyethylene (LDPE) mixed with diesel fuel in volume ratios WPO10, WPO20, WPO30, WPO40, and WPO50. In addition, a performance test was carried out on the single-cylinder diesel engine test bench. The results showed that performing the diesel engine with the addition of WPO increased the average power and torque by 5% and 3%, thereby producing a higher heating value. Furthermore, the concentration of WPO also reduces the level of specific fuel consumption to be more efficient, with a decrease in thermal efficiency. In conclusion, plastic waste pyrolysis oil is a promising alternative fuel applicable to a diesel engine.

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 score0.277
Threshold uncertainty score0.646

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 head0.228
Teacher spread0.207 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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