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Record W2331494041 · doi:10.1115/ipc2012-90691

Modeling the Static Cooling of Wax–Solvent Mixtures in a Cylindrical Vessel

2012· article· en· W2331494041 on OpenAlexaff
Sridhar Arumugam, Adebola S. Kasumu, Anil K. Mehrotra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWaxMaterials scienceSolubilityPhase (matter)Heat transferDeposition (geology)SolventPrecipitationParaffin waxChemical engineeringThermodynamicsChemistryComposite materialOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Under subsea conditions, the transportation of ‘waxy’ crude oil through pipelines is accompanied by the precipitation and deposition of higher paraffinic compounds as solids (waxes) onto the cooler surfaces of the pipeline. Wax deposition is more pronounced during shut down of a pipeline since the fluid is held at static conditions. In this study, the static cooling of wax–solvent mixtures in a cylindrical vessel was modeled as a moving boundary formulation involving liquid–solid phase transformation. The deposition process during the transient cooling was treated as a partial freezing/solidification process. Also, the effect of the mixture composition and the cooling rate on the Wax Precipitation Temperature (WPT) or the solubility curve of the wax–solvent mixture was taken into consideration when the bulk liquid phase temperature was lowered below the WAT of the initial mixture composition. The predictions for the transient temperature profiles in the liquid and the deposit region, and the location of the liquid–deposit interface were validated with recently reported experimental results [19]. The predictions were also compared with the predictions for the gelling behavior of wax–solvent mixtures under static cooling reported by Bidmus [19]. The predictions for the temperature profile at seven thermocouple locations and the location of the liquid–deposit interface were in agreement with the experimental results and signified the important role of the solubility curve. The mathematical model presented was based on heat transfer considerations and regarded the deposition process to be thermally driven.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.020
GPT teacher head0.270
Teacher spread0.250 · 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 designSimulation or modeling
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

Citations5
Published2012
Admission routes1
Has abstractyes

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