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Record W2094495115 · doi:10.1021/ef700769v

Biodiesel Production Using Ultralow Catalyst Concentrations

2008· article· en· W2094495115 on OpenAlexafffund
André Y. Tremblay, Peigang Cao, Marc A. Dubé

Bibliographic record

VenueEnergy & Fuels · 2008
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiodieselTransesterificationBiodiesel productionMethanolSodium hydroxideCatalysisChemistryDiesel fuelFatty acid methyl esterVegetable oilPulp and paper industryGlycerolVegetable oil refiningOrganic chemistryChemical engineering

Abstract

fetched live from OpenAlex

Biodiesel is a nontoxic, biodegradable, renewable diesel fuel obtained from lipid feedstocks. The most common production process for biodiesel is through the batch transesterification of vegetable oils with methanol. In the batch process, excess catalyst which is used to drive the reaction to completion can result in high material and processing costs and the degradation of components found in lipid feedstocks which impart color to the resulting biodiesel. Much work on heterogeneous catalysts has been done in the past decade; however, most of the work was done at temperatures and pressures well above those used in atmospheric batch processes. Under these conditions, the reaction rates of homogeneously catalyzed reactions would be enhanced. However, equilibrium limitations do require substantial amounts of catalyst to be present to drive the reactions to completion. Reducing the amount of homogeneous catalysts required for the transesterification would reduce the need for washing of the fatty acid methyl esters (FAME) product and the formation of soap in the biodiesel and improve the color of the resulting glycerol and in many cases the resulting biodiesel. In the present study, the transesterification of canola oil with methanol was investigated at varying catalyst concentrations and residence times (RT) in a continuous membrane reactor. Prior to all experiments, the free fatty acid in the canola oil was neutralized with sodium hydroxide. Experiments were performed at 0.0, 0.01, 0.03, 0.05, 0.1, 0.5, and 1 wt % sodium hydroxide on an oil basis. The methanol:oil mole ratio used in the experiments was 24:1. It was found that a base catalyst concentration above 0.05 wt % for a 1 h residence time (RT) and above 0.03% for a 2 h RT resulted in the steady-state biodiesel production via the membrane reactor. Such catalyst amounts are 10−33 times lower than those employed in the industrial production of biodiesel (e.g., 0.5−1 wt % sodium hydroxide concentration). Mono- and triglycerides were not detected in any of the biodiesel produced in this single reaction step process. The RT in the reactor displayed no obvious effect on permeate composition. The results demonstrated that maintaining the phase separation between the oil and methanol at all times during the reaction and the ability of the membrane to separate these phases were key in obtaining high quality product. Unreacted triglycerides and unsaponifiable, oleophilic impurities in the lipid feedstock were not integrated into the final biodiesel as in conventional processes due to the presence of two phases in the reactor. The results illustrate the advantages in using a membrane reactor to produce biodiesel, where products are continuously removed from the reactor in a different phase than the lipid reactant.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.029
GPT teacher head0.221
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations52
Published2008
Admission routes2
Has abstractyes

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