Contribution to Management of Safety Instrumented Systems (SIS) in Furnace Water Cooling Systems on Elkem Plants
Bibliographic record
Abstract
Cooling systems are widely used across different industries in order to keep the industrial processes running and ensure safety. The cooling systems are a critical part of industrial furnaces, particularly Furnace Water Cooling Systems (FWCS) are used for providing process cooling water to water-cooled furnaces. When the cooling system is out of control, damage can begin to occur to the cooling system and the components it protects. One of the most common problems associated with cooling systems is water leakage. In This Master Thesis, literature study and analysis of information on accidents related to FWCS leakages showed that it is important to look for the opportunities of improvements within safety measures and initiate projects for implementation of possible feasible safety solutions along with the existing traditional flow-based leakage detection system. Nowadays, the market and associated industrial experience, offers several solutions for water leakage detection. In this Master Thesis we discussed a pressure-based method for very small leaks detection which has already been successfully employed by a Canadian company Nickel Smelter Vale Ltd. We suggested to implement a SIF based on this method as a pilot project for some critical water-circuits (in terms of both eruption and explosion problems) as an additional measure of risk mitigation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".