MétaCan
Menu
Back to cohort
Record W2063610829 · doi:10.1117/12.417390

<title>Near-IR optical process sensor for electric arc furnace pollution control and energy efficiency</title>

2001· article· en· W2063610829 on OpenAlexaff
Jason J. Nikkari, Murray J. Thomson

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2001
Typearticle
Languageen
FieldChemistry
TopicSpectroscopy and Laser Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsElectric arc furnaceElectric arcCalibrationExhaust gasAnalytical Chemistry (journal)Materials scienceMethaneProcess controlChemistryProcess (computing)MetallurgyElectrodePhysicsComputer science

Abstract

fetched live from OpenAlex

12 An optical near-IR process sensor for electric arc furnace pollution control and energy efficiency has been proposed. A near-infrared laser has performed simultaneous in-situ measurements of CO (1577.97 nm), H<SUB>2</SUB>O (1577.8 nm and 1578.1 nm) and temperature in the exhaust gas region above a laboratory burner fueled with methane and propane. The applicable range of conditions tested is representative of those found in a commercial electric arc furnace and includes temperatures from 1250 - 1750 K, CO concentrations from 0 to 10% and H<SUB>2</SUB>O concentrations from 3 to 27%. Two- tone frequency modulation was used to increase the detection sensitivity. An analysis of the method's accuracy has been conducted using 209 calibration and 105 unique test burner setpoints. Based on the standard deviation of differences between optical predictions and independently measured values, the minimum accuracy of the technique has been estimated as 36 K for temperature, 0.47% for CO and 3.4% for H<SUB>2</SUB>O. This accuracy is sufficient for electric arc furnace control. The sensor's ability to non-intrusively measure CO and temperature in real time will allow for improved process control in this application.

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 score0.416
Threshold uncertainty score0.687

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicSpectroscopy and Laser ApplicationsFrench-language works237,207