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Record W1975902100 · doi:10.1021/ie0601706

Modeling of Deposition from “Waxy” Mixtures in a Pipeline under Laminar Flow Conditions via Moving Boundary Formulation

2006· article· en· W1975902100 on OpenAlexafffund
Nitin V. Bhat, Anil K. Mehrotra

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2006
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of Calgary
FundersWestern Canada Research GridUniversity of Calgary
KeywordsLaminar flowDeposition (geology)MechanicsHeat transferThermodynamicsReynolds numberEutectic systemMaterials scienceFlow (mathematics)Steady state (chemistry)ConvectionInletPipe flowMass transferChemistryComposite materialGeologyTurbulencePhysics

Abstract

fetched live from OpenAlex

A mathematical model, based on the moving boundary approach, is developed for the growth of the deposit from paraffinic mixtures due to heat transfer in a pipeline. The model extends a recent study on the deposition from “waxy” mixtures by including unsteady-state energy balance and heat transfer in both radial and axial directions for hydrodynamically established laminar flow. Numerical solutions were obtained for the growth of deposit with time, both radially and axially, from a binary eutectic mixture of n -C 16 H 34 and n -C 28 H 58 . Two different scenarios for the initiation of the deposition process were investigated. Even though the transient results for the temperature profile and deposit thickness differed significantly, identical steady-state predictions were obtained for the two scenarios. For a constant pipe-wall temperature, the steady-state deposit thickness was predicted to increase with the pipe length. The predicted deposit thickness was lower for a higher inlet mixture temperature, pipe-wall temperature, and Reynolds number. The trends in model predictions were compared with the published results from deposition experiments performed on similar waxy mixtures.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.909

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.036
GPT teacher head0.298
Teacher spread0.262 · 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

Citations32
Published2006
Admission routes2
Has abstractyes

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