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Record W4401371389 · doi:10.1016/j.heliyon.2024.e35820

Performance and emission analysis of cassava peel waste pyrolysis oil-hydrogen-diesel blends in a compression ignition engine

2024· article· en· W4401371389 on OpenAlexfundno aff
Luis Estrada-Diaz, Brando Hernández-Comas, Antonio Bula, Arturo González‐Quiroga, Jorge Duarte Forero

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
FundersMichael Cuccione FoundationWeyerhaeuser CompanyUniversidad de la Costa
KeywordsIgnition systemDiesel engineDiesel fuelPyrolysisWaste managementBiodieselCarbureted compression ignition model enginePyrolysis oilCompression (physics)Materials scienceOil analysisPulp and paper industryBiofuelEnvironmental scienceChemistryCompression ratioOrganic chemistryCombustionEngineeringComposite materialAutomotive engineeringDiesel cycleCatalysisMetallurgy

Abstract

fetched live from OpenAlex

As the world moves away from fossil fuels and embraces sustainable energy sources, the need for sustainable fuels for transportation becomes paramount. This study investigates the effects of pyrolysis oil derived from cassava peel waste (CPO), hydrogen (H), and diesel (D) blends as a partial substitute for low-displacement compression ignition engines. We tested three blends – CPO25, CPO25H5, and CPO25H10 – against neat diesel operation at engine speeds of 3400 rpm, 3600 rpm, and 3800 rpm and torques of 4 Nm, 6 Nm, and 8 Nm. Our findings reveal that while energy efficiency decreased with CPO25 compared to D100 operation, adding H2 increased energy efficiency. The highest increase was 7.8 % for CPO25H5 and 16 % for CPO25H10 compared to CPO25. Exergy efficiency also decreased with CPO25 compared to D100, but adding H2 compensated for this reduction. The highest increase was 8.0 % for CPO25H5 and 17 % for CPO25H10 compared to D100. CPO25H10 showed an increase of 8.1 % in combustion pressure and 9.9 % in heat release rate compared to CPO25. Emissions analysis also revealed that CO emissions were considerably lower with CPO and H2 than with D100, with the highest decrease of 11 % with CPO25H10. CO2 and hydrocarbon emissions followed the same trend as CO. Although NOx emissions slightly increased, the benefits of using pyrolysis oil-H2-diesel blends as a partial substitution fuel for low-displacement compression ignition engines are evident.

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 score0.729
Threshold uncertainty score0.408

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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