The effect of process parameters on audible acoustic emissions from high-shear granulation
Bibliographic record
Abstract
Product quality in high-shear granulation is easily compromised by minor changes in raw material properties or process conditions. It is desired to develop a process analytical technology (PAT) that can monitor the process in real-time and provide feedback for quality control. In this work, the application of audible acoustic emissions (AAEs) as a PAT tool was investigated. A condenser microphone was placed at the top of the air exhaust on a PMA-10 high-shear granulator to collect AAEs for a design of experiment (DOE) varying impeller speed, total binder volume and spray rate. The results showed the 10 Hz total power spectral densities (TPSDs) between 20 and 250 Hz were significantly affected by the changes in process conditions. Impeller speed and spray rate were shown to have statistically significant effects on granulation wetting, and impeller speed and total binder volume were significant in terms of process end-point. The DOE results were confirmed by a multivariate PLS model of the TPSDs. The scores plot showed separation based on impeller speed in the first component and spray rate in the second component. The findings support the use of AAEs to monitor changes in process conditions in real-time and achieve consistent product quality.
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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 | 0.003 |
| 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) | 0.001 | 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 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".