Skewed gas flow technology: a method to improve precipitator performance
Bibliographic record
Abstract
The application of Skewed Gas Flow Technology (SGFT) by Stothert Engineering Ltd. in Vancouver has demonstrated that particulate emissions can be reduced substantially by departure from standards calling for uniform gas flow distribution. Controlled skewed flows implemented in precipitators serving approximately 7000 MW of coal-fired electrical power generation plus installations in the pulp and paper industry have achieved reduction in particulate emissions from approximately 20 to over 70%. In most cases, these emissions reductions have been achieved on installations previously utilizing uniform gas flow standards. SGFT, utilizing inexpensive flow modifications, represents a major advancement in precipitator performance improvement technology. L'application d'une technologie d'alimentation non uniforme du gaz par Stothert Engineering Ltd. (Vancouver) a demontre qu'on peut reduire substantiellement les emissions de particules en s'ecartant des normes exigeant une distribution uniforme du gaz alimente. Un debit non uniforme controle dans les precipitateurs offrant environ 7000 MW de generation d'energie electrique alimentee au gaz dans l'industrie des pâtes et papiers a permis de reduire les emissions de matieres particulaires d'environ 20 a 70%. Dans la plupart des cas, cette reduction a ete obtenue dans des usines qui utilisaient auparavant des normes d'alimentation uniforme du gaz. La technologie d'alimentation non uniforme, qui n'exige que des modifications peu dispendieuse au debit, represente un progres majeur en matiere de technologie d'amelioration de la performance des precipitateurs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".