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Effect of Operating Conditions of the Extraction Process on the Physical Properties of Lubricating Oil

2015· article· en· W2055367574 on OpenAlexvenueno aff
Amirhossein Mehrkesh, Touraj Tavakoli, Mohammad Sadegh Hatamipour

Bibliographic record

VenueJournal of Applied Solution Chemistry and Modeling · 2015
Typearticle
Languageen
FieldEngineering
TopicLubricants and Their Additives
Canadian institutionsnot available
Fundersnot available
KeywordsSettlingExtraction (chemistry)Yield (engineering)SolventChromatographyPulp and paper industryChemistrySettling timeProcess (computing)Base oilProcess engineeringChemical engineeringMaterials scienceOrganic chemistryEnvironmental scienceMetallurgyComposite materialEnvironmental engineeringComputer science

Abstract

fetched live from OpenAlex

Lubricating base oil is commonly extracted from lube-oil cut, a petroleum cut, with the use of an aromatic solvent. Aromatic content of the final product is an important criterion specifying the product quality. The aromatic removal process to produce the lubricating oil should be carried out in a Liquid-Liquid extraction column. In a typical solvent extraction process, solvent to feed ratio, solvent and feed temperatures, agitation rate, and settling time could directly affect the yield of extraction. In the current study, the effect of agitation rate and settling time on the yield of extraction was studied. It was found that a settling time of 2hrs and an agitation rate of 430 RPM to be the optimum parameters of the extraction process.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.259
Teacher spread0.236 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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