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Record W2772950198 · doi:10.1109/pcicon.2017.8188733

New alternatives to manage hot surface ignition temperatures for trace heating in explosive atmospheres

2017· article· en· W2772950198 on OpenAlexaff
Dan Caouette, Jim Parks, Matt Aurini

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTRACE (psycholinguistics)Explosive materialFlexibility (engineering)Electric heatingIgnition systemComputer scienceProcess engineeringProcess (computing)Mechanical engineeringEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

The design of high temperature electric trace heating in hazardous areas can be a major challenge. This is particularly true when process temperatures approach the area classification limit, restricting the allowable temperature differential between what is heated and the surface temperature of the electric trace heaters. While there are methods that can be applied to address the challenges, they have traditionally been bound by the attributes of the trace heaters. In many cases, this leads to designs that are difficult to install, maintain, and often increases the cost of the overall solution. This paper will present alternative methods to deal with these challenging situations using recently improved heater constructions and controller algorithms. Used with engineering design software that can accurately predict heater surface temperatures, these options provide the design engineer with improved flexibility in creating solutions. Examples of current projects underway will illustrate that designs incorporating these new technologies result in significant improvements in constructability, and a reduction in installed costs over conventional approaches.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.682
Threshold uncertainty score0.379

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 head0.025
GPT teacher head0.284
Teacher spread0.259 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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