<title>Near-IR optical process sensor for electric arc furnace pollution control and energy efficiency</title>
Bibliographic record
Abstract
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), H2O (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 H2O 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 H2O. 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".