Bubble Detection in Gas–Solid Separation Fluidized Beds Based on Deep Learning
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In dry coal beneficiation with gas–solid fluidized beds, bubble dynamics critically affects fluidization stability and separation efficiency. This study proposed an improved YOLOv8-based bubble detection model to achieve accurate and real-time bubble monitoring. The model integrates a Multi-Head Self-Attention (MHSA) mechanism to enhance global feature extraction, a multiscale feature fusion structure (BiFPN-CONCAT) to achieve efficient feature integration, and an Involution-based Decoupled Head to improve detection precision while reducing computational complexity. Experimental validation demonstrated that the proposed model achieved superior detection performance, with precision, recall, and [email protected] being 99.1, 96.0, and 95.5, respectively, outperforming the YOLO series and mainstream detectors such as Faster R-CNN and Mask R-CNN. Moreover, as was revealed through the analysis of 120 experimental data sets, average bubble area was strongly negatively correlated with ash content ( r = − 0.72 ), while bubble number was positively correlated with clean coal yield ( r = + 0.90 ) . A regression model based on bubble features achieved a coefficient of determination of R 2 = 0.89, confirming their predictive value for separation performance. These findings demonstrate that the proposed model not only ensures high-precision bubble detection but also provides new insights into the coupling between fluidization dynamics and the beneficiation efficiency. This study offers theoretical and technical support for intelligent dry coal separation systems.
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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 | 0.000 | 0.001 |
| Open science | 0.001 | 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".