Upgrade of a multi-channel active noise control system for an industrial stack
Bibliographic record
Abstract
Active noise control has been studied in the 1990s as an innovative way to reduce the noise in specific situations. Some applications are well known today and found commercial success like noise-canceling headphones. However, the use of active noise control in industrial applications is more complex, thus being an uncommon solution in this field. The use of active noise control for industrial stack noise is one of these applications. One of the first large-scale implementation has been set up at the end of the 1990s. This system was a 10-channel active noise control system installed on a 1.8 m wide chimney to attenuate a 320 Hz pure tone. At that time, an 8 dB noise reduction was achieved at error microphones. Fifteen years later, it has been decided to upgrade the system with the latest generation of digital signal processor (DSP) allowing a real-time optimization and better tracking speed. This paper describes the overall system and the updated multi-channel active noise controller developed for this application. It also presents the improvements, the achieved noise reduction, and the associated environmental benefits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".