Identification of abnormal conditions for gold flotation process based on multivariate information fusion and double‐channel convolutional neural network
Bibliographic record
Abstract
Abstract Accurate and in‐time working condition identification plays a great role in industrial processes. However, most of the current flotation process identification models only use the characteristics of flotation froth as an identification basis, which often causes identification errors due to the instability of the froth, and a large amount of process data is not fully utilized. In this paper, an abnormal condition identification method based on multivariate information fusion and double‐channel convolutional neural network (double‐channel CNN) is proposed to achieve higher accuracy. First, a double‐channel CNN is used to extract depth features from different distributions of froth images and process data in parallel. Then, double normalized attention mechanism (double normalized AM) and multivariate information fusion methods are used to attach weights to the features and fuse them so as to ensure a higher response of key features and increase the reliability of the identification model. The method shows better performance than existing methods in offline simulations and has been validated online at a mineral processing plant in Shandong.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".