Hazard Analysis with STPA Methods: Application to Mould Level Control Within Continuous Casting Free Stream Operations
Bibliographic record
Abstract
In the continuous casting process, maintaining precise control over the mould level is essential to ensure product quality, prevent defects, and optimize operational efficiency.Mould Level Control (MLC) in Free Stream operations presents unique challenges, as it requires accurate and stable control amidst dynamic process variations and complex interdependencies between sensors, actuators, and controllers.This paper presents a case study conducted at a steel plant located in Bellara, El Milia -Jijel, Algeria, which utilizes a 120-ton Electric Arc Furnace (EAF) operating at a tap temperature of approximately 1630℃.The process is controlled via a Siemens PLC-based automation system.Continuous casting is performed using a curved-type machine (3BLC 0905), featuring five strands, a casting section of 150 ×150 mm, and a maximum casting speed of 3.5 m/min.The system includes a ladle turret, 30-ton tundish, mould, and withdrawal system, and is used to produce low-carbon steel grades (C < 0.13%, Al < 0.006%).System-Theoretic Process Analysis (STPA) is applied to identify and mitigate hazards in MLC systems under Free Stream conditions.The analysis highlights unsafe control actions (UCAs), actuator delays, and sensor inaccuracies, and proposes improvements in control logic, calibration, and response strategies to enhance system safety and reliability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".