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Record W2741696436 · doi:10.1002/cjce.22949

Investigation of wax deposit ‘sloughing’ from paraffinic mixtures in pipe flow

2017· article· en· W2741696436 on OpenAlexafffundvenue
Chandni Sinha, Anil K. Mehrotra

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSloughingWaxCoolantMaterials scienceMass transferHeat transferFlow (mathematics)Volumetric flow ratePhase (matter)Layer (electronics)Composite materialChemistryThermodynamicsChromatographyMechanics

Abstract

fetched live from OpenAlex

Abstract Deposit ‘sloughing’ from ‘waxy’ crude oils has been described in the literature as a possible mechanism, leading to partial or complete dislodging of the deposit from the pipe wall due to changes in flow parameters. A bench‐scale flow loop apparatus was used to investigate ‘sloughing’ with prepared single‐phase ‘waxy’ mixtures of a multicomponent paraffinic wax dissolved in a multicomponent solvent. Experiments were performed to study the changes in the deposit‐layer thickness due to step increments in the ‘waxy’ mixture flow rate, the mixture temperature, and the coolant temperature. It was observed that the deposit‐layer thickness decreased with an increase in each of the three parameters; however, a complete or sudden dislodging of the deposit‐layer did not occur in any of the experiments. A steady‐state heat‐transfer model was used to predict the variation in the deposit mass or thickness due to changes in the selected parameters. In each case, the step‐wise decrease in the deposit thickness, as observed experimentally, was predicted to be caused by changes in the thermal resistance and/or thermal driving force.

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)0.0000.000
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)0.0010.000

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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

Citations13
Published2017
Admission routes3
Has abstractyes

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