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Record W2315334802 · doi:10.1021/ie5003476

Simulation Analysis of Energy Performance of Distillation-, Stripping-, and Flash-Based Methanol Recovery Units for Biodiesel Production

2014· article· en· W2315334802 on OpenAlexafffund
Firuz A. Philip, Amornvadee Veawab, Adisorn Aroonwilas

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Regina
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMethanolDistillationStripping (fiber)BiodieselFlash (photography)ChemistryTransesterificationFractional distillationVacuum distillationContinuous distillationBiodiesel productionProcess engineeringMaterials scienceChromatographyOrganic chemistryCatalysisEngineering

Abstract

fetched live from OpenAlex

This work evaluates and compares performance of distillation-, stripping-, and flash-based methanol recovery units for biodiesel production in terms of energy requirement and purity of the recovered methanol. The evaluation was carried out by simulating the transesterification process coupled with methanol recovery units using the process simulator, Aspen Plus. The results show that the energy requirement of all tested methanol recovery units is influenced by process parameters in similar manners. The heat duty per mass of methanol recovered increases with % methanol recovery, operating pressure and reflux ratio (in case of distillation), but decreases with methanol-to-oil ratio. The flash units are the most energy efficient, followed by stripping and distillation units. They can produce pure recovered methanol, but may not achieve as high methanol recovery target (98%) as the stripping and distillation units do. The double-flash unit has no advantage over the single-flash unit in the aspect of energy requirement.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.180
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.093
GPT teacher head0.304
Teacher spread0.210 · 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 designSimulation or modeling
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
Published2014
Admission routes2
Has abstractyes

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