MétaCan
Menu
Back to cohort
Record W1989039531 · doi:10.1021/ef0603784

Deposition under Turbulent Flow of Wax−Solvent Mixtures in a Bench-Scale Flow-Loop Apparatus with Heat Transfer

2006· article· en· W1989039531 on OpenAlexaff
Nelson Fong, Anil K. Mehrotra

Bibliographic record

VenueEnergy & Fuels · 2006
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWaxTurbulenceReynolds numberLaminar flowMass transferCoolantChemistryDeposition (geology)ThermodynamicsMaterials scienceAnalytical Chemistry (journal)ChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

A flow-loop apparatus, incorporating a co-current double-pipe heat exchanger, was developed for investigating the deposition of solids from solutions of a multicomponent wax in a paraffinic solvent under turbulent flow. The deposition experiments were performed at Reynolds numbers of 10 000−31 000 with 7, 10, and 15 mass % wax−solvent mixtures at different hot and cold stream temperatures. In all experiments, the deposit was formed rapidly such that a thermal steady state was attained within 20−30 min. The deposit mass decreased with an increase in the Reynolds number, the wax−solvent mixture temperature, and the coolant temperature. The data were analyzed with a steady-state heat-transfer model, which confirmed the deposit mass to depend upon the relative magnitudes of the thermal resistances in series as well as the fractional temperature drop across the deposit layer. The estimated liquid−deposit interface temperature was shown to be close to the wax appearance temperature of each wax−solvent mixture. The average thermal conductivity of the deposit was estimated to be 0.35 W m - 1 K - 1 . The gas chromatograph analysis of deposit samples showed their wax content and carbon-number distribution to vary with the deposition time and Reynolds number. Overall, the results of this study confirm that the deposition process from “waxy” mixtures is primarily thermally driven under both laminar and turbulent flow.

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 score0.046
Threshold uncertainty score0.750

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 head0.005
GPT teacher head0.198
Teacher spread0.193 · 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

Citations48
Published2006
Admission routes1
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicPetroleum Processing and AnalysisFrench-language works237,207