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Record W2937851636 · doi:10.31399/asm.cp.itsc2019p0520

Techno-Economic Assessment of Coating-Based Resistive Heating Systems versus Conventional Heat Tracers

2019· article· en· W2937851636 on OpenAlexaff
Kingsley Ngaokere, Amit Kumar, André McDonald, Daniel Hayden

Bibliographic record

VenueThermal spray · 2019
Typearticle
Languageen
FieldEngineering
TopicHigh-Temperature Coating Behaviors
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCoatingMaterials scienceNichromeFabricationThermal sprayingTracingThermal energy storageResistive touchscreenElectric heatingComposite materialNuclear engineeringMetallurgyEngineeringComputer scienceElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The economic feasibility of using thermal-sprayed heat generating coatings for temperature control in steel pipes was investigated. A data-intensive model was developed to compare fabrication, installation, operation, and maintenance expenditures with those of conventional heating cables. The multi-layered coating consists of flame-sprayed Al2O3 and NiCr layers and cold-sprayed copper. Scalability factors were incorporated in the model to estimate the total projected costs for fabricating the coatings as opposed to installing heat tracing. Although material costs for the coating and heat tracing were approximately the same, the cost of fabrication for the coating was higher due mainly to labor expenses. However, the coating-based system was found to be more energy efficient than heat tracing due to the good adhesion and reduced thermal contact resistance between the heating elements and pipe.

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)0.003
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
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 head0.011
GPT teacher head0.256
Teacher spread0.245 · 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

Citations4
Published2019
Admission routes1
Has abstractyes

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