Hot Works During Pre-Turnaround Activities : Mitigation Risk
Bibliographic record
Abstract
Abstract Milazzo Refinery (RaM) has developed a new safety system to minimize the risks related to "hot works" (mainly explosion risks) during normal unit operations. The good practice was for the first time utilized during Fluid Catalytic Cracking pre-turnaround and turnaround phase from Milazzo Refinery. At that time, RaM implemented, for the first time, the integration between the existing network system of fixed hydrocarbon detectors with the welding machines through the Distributor Control System (DCS). This integration allowed to immediately switch off the power supply to the welding machines in case of detection of explosive atmosphere. Moreover, in order to cover as many welding points as possible, several mobile detectors were installed and integrated in the gas detectors system. The whole process is represented in the scheme in Figure 1. The main benefits of such integrated safety system were as by following: Mitigation of the risks related to hot work execution during normal operations with consequent safety improvement. This also allows, during turnaround, to maximize the preturnaround activities so that its duration can be profitably reduced;Increase the quality of the measure which is continuously detected and monitored by electronic devices (operator originally used to detect if the atmosphere was hydrocarbon free only before "hot work" execution. During the pre-turnaround phase, with FCC Plant in normal operation, more than 200 tons of piping steel and more than 400 tons of steel structures were installed and welded without any incident or injury to direct personnel or contractors. The successfully implementation of the integrated gas detectors system has become a new standard within the refinery to increase safety in the execution of "hot works " during normal unit operations, strongly mitigating the risks related to explosive atmosphere. Moreover, this system also allowed to speed up the execution of the "hot work" hence it became a RaM standard for similar process.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".