MétaCan
Menu
Back to cohort
Record W2130596603 · doi:10.1002/cjce.22158

Kinetics of transesterification reaction using CAO/AL<sub>2</sub>O<sub>3</sub> catalyst synthesized by sol‐gel method

2015· article· en· W2130596603 on OpenAlexvenueno aff
Gholamreza Moradi, Yegane Davoodbeygi, Majid Mohadesi, Shokoufe Hosseini

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMethanolTransesterificationBiodieselCatalysisSoybean oilBiodiesel productionReaction rate constantActivation energyChemistryCalcium oxideReaction rateNuclear chemistryGlycerolKineticsOrganic chemistry

Abstract

fetched live from OpenAlex

Calcium oxide is one of the appropriate catalysts for biodiesel production. In this study, 40 wt. % CaO/Al 2 O 3 catalyst was used. Transesterification reaction was performed in optimal condition presented in the previous study (0.05 molar nitric acid and gelation temperature of 70 °C) in a 250 mL two‐necked flask. All the experiments were carried out in the presence of soybean oil, methanol (methanol to oil molar ratio of 12:1), and catalyst concentration of 6 wt. %. Stirrer speed was set at 350 rpm. This study investigated the effects of reaction temperature and reaction time on produced biodiesel conversion. Methyl ester conversion changes in all temperatures and across different times indicated pseudo‐first order kinetic, so, first, the observed rate constant was obtained at various temperatures. Then, observed activation energy of soybean oil methanolysis in the presence of CaO/Al 2 O 3 catalyst was determined. The results show that maximum errors of model are in primary times because methanol is insoluble in soybean oil. At the other times and by biodiesel and glycerol production, there was increased solubility of methanol in oil. The methyl esters production rate is related to diffusion of methanol in oil film and reaction rate constants which are calculated these constants.

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 score0.063
Threshold uncertainty score0.744

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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicBiodiesel Production and ApplicationsFrench-language works237,207