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

2015· article· en· W2055367574 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

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 score0.095
Threshold uncertainty score0.148

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 head0.259
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