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Record W1491218922 · doi:10.5539/mas.v9n7p93

The Production of Biofuels from Coconut Oil Using Microwave

2015· article· en· W1491218922 on OpenAlexvenueno aff
Andi Suryanto, Suprapto Suprapto, Mahfud Mahfud

Bibliographic record

VenueModern Applied Science · 2015
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsBiodieselBiofuelTransesterificationMethanolBiodiesel productionRaw materialPulp and paper industryEnvironmentally friendlyLauric acidCoconut oilDistillationPetrochemicalYield (engineering)Renewable energyAcid valueCatalysisChemistryMaterials scienceOrganic chemistryWaste managementFatty acidFood scienceBiochemistry

Abstract

fetched live from OpenAlex

Biofuels including biodiesel, an alternative fuel, is renewable, environmentally friendly, non-toxic and lowemission energy. The raw material used in this work was coconut oil, which contained saturated fatty acids about90% with medium chain (C8-C12), especially lauric acid and myristic acid. Reaction was conducted in batchreactor assisted by microwave. The purpose of this research was to study the effect of power and NaOH catalystin transesterification enhanced by microwave and to obtain a biofuels (biodiesel and biokerosene) derived fromcoconut oil. The reaction was performed by mixing oil and methanol with mole ratio of 1:6, catalystconcentration of 1% w/w with setting electrical power at 100, 264, 400, 600 and 800 W. The reaction time wasconditioned at of 2.5, 5, 7.5, 10 and 15 min. The results showed that microwave could accelerate thetransesterification process to produce biodiesel using NaOH catalyst. The highest yield of biodiesel was 97.76 %,or 99.05 % conversion at 5 min reaction, meanwhile biokerosene was 48% after distillation.

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 score0.139
Threshold uncertainty score0.224

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 head0.045
GPT teacher head0.245
Teacher spread0.200 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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