Design of an aqueous particle sensor (APS) for optimizing inclusion removal by bubbles in tundish operations
Bibliographic record
Abstract
Due to the increasingly stringent cleanliness requirements for final steel products, there has been growing interest in using micro gas bubble injection in the steelmaking tundish for the removal of inclusions with diameter smaller than 50μm. However, several technological barriers prevent adoption of this technique in industry. These are related to the generation of micro bubbles, measurement of bubble size distributions, and optimization of bubble injection conditions.In the present study, a novel Aqueous Particle Sensor (APS) IV system was developed for in-situ, on-line detection of bubbles generated by a newly designed ladle shroud located at McGill Metal Processing Centre (MMPC). Measurement results from the sensor were validated against bubble size data collected through a high speed camera. The Aqueous Particle Sensor (APS) III system was also used experimentally under various gas injection conditions to optimize inclusion removal efficiency. Control variable and orthogonal experiments were designed to assess the dependence of the final steel cleanliness and bubble size on key experimental parameters. These were the air inlet flowrates, the distance from the injection port to the slide gate, and the number of injection ports.The investigation demonstrated that the APS system can be successfully used for micro-bubble detection. Through this novel sensor it was shown that multi-port injection using a small air inlet flowrate and a shorter distance from the slide gate can be used to promote the formation of small micro-bubbles. However, optimizing the gas injection flowrate for inclusion removal requires a compromise between reducing the bubble size and increasing the number of micro-bubbles.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".