CONTINUOUS EMISSION MONITORING OF METALS IN FLUE GASES BY ICP-OES: ROLE OF CALIBRATION AND SAMPLE GAS
Bibliographic record
Abstract
Several methods have been used over the past few years to continuously monitor the elemental pollutants in the flue gases released by industrial processes. The most promising apply spectroscopic detection to an argon inductively coupled plasma (ICP) into which the gas to be analysed is injected. The main problem with these methods concerns their reliability for example, a recent comparison with a reference method using filter sampling has exposed systematic discrepancies. This could be due to deposition phenomena inside the sampling system and to the calibration procedure. The latter procedure generally consists in the nebulization and desolvation of a standard solution to obtain dry aerosols. These aerosols are then carried by pure air and injected into the plasma. A detailed study of the calibration procedure showed that errors can be reduced by ensuring that the composition of the carrier gas is carefully controled to be the same as that of the gas in which the heavy metals are analysed. It is also possible to choose operating conditions that are insensitive to changes in sample gas composition, but the limits of detection are degraded. The ICP technique has been evaluated using two field tests: at a pilot plant for fly ash vitrification, and at a coal-fired power plant. These tests confirm that the control of the gas composition is an essential point, which has not been sufficiently taken into account in the past.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".