Multi-scale multi-task neural network combined with transfer learning for accurate determination of the ash content of industrial coal flotation concentrate
Bibliographic record
Abstract
Ash content is a key indicator to evaluate coal flotation concentrate quality and adjust flotation process parameters, which could be determined by analyzing froth images. In this research, a multi-scale multi-task neural network (MSTNet) was developed to realize accurate determination of the ash content of industrial coal flotation concentrate by analyzing froth images. Furthermore, transfer learning is used to further improve model accuracy for low-resolution images. Results obtained using industrial data show that MSTNet achieves a higher prediction accuracy while requiring less computations than previous models. It reaches the maximum R 2 of 0.9063 with a processing time of 0.0035 seconds per image, while its competitors only reach the maximum R 2 of 0.7231 with a processing time of 0.0038 seconds per image. This suggests that MSTNet surpassing its competitors in both accuracy and speed. Furthermore, MSTNet achieves the minimum MAPE of 0.0300, indicating that MSTNet has a mean relative prediction error of ± 3 %. This proves the high prediction accuracy of MSTNet. These results indicate that the proposed MSTNet holds great promise for practical applications. Its practical application will lead to more efficient and intelligent coal production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 | 0.001 |
| 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".