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Record W3177597382 · doi:10.18280/acsm.450304

Effect of EGR on Performances and Emissions of DI Diesel Engine Fueled with Waste Plastic Oil: CDF Approach

2021· article· en· W3177597382 on OpenAlexvenueno aff
Khatir Naima, Younes Menni, Mounir Alliche, Giulio Lorenzini, Hijaz Ahmad, Abdelkrim Liazid

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2021
Typearticle
Languageen
FieldEngineering
TopicThermochemical Biomass Conversion Processes
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental scienceWaste managementDiesel engineDiesel fuelNOxPlastic wasteFraction (chemistry)Automotive engineeringEngineeringChemistryCombustion

Abstract

fetched live from OpenAlex

Recently, there is an increasing interest in the pyrolysis of waste plastic into usable fuel as a friendly environment method for waste plastic disposal. The existing literature from various studies stated that the major problem related to the use of WPO in diesel engines is the high NOx emissions level. This paper aims to remedy this problem by suggesting the best EGR percentage with the advanced optimum injection timing. Primary, 5 EGR percentage fractions are considered: 0%, 5%, 15%, 20% and 25% percent. The results showed that 25% is the best percentage regarding emissions. However, a significant reduction in mean in-cylinder pressure, temperature, and heat release rate was depicted with the EGR fraction increase. Injection timing is advanced to recoup the decrease in performance. The results showed that 25% of EGR and advanced injection timing by 5 degrees would be better for performances and emissions of DI diesel engine while running with waste plastic oil as an alternative fuel.

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 score0.015
Threshold uncertainty score0.430

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.213
Teacher spread0.199 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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