Accident causes involving pressure vessels: A case study analysis with STAMP model
Bibliographic record
Abstract
Although most companies comply with laws and regulations and use the latest technologies, tragic accidents involving pressure vessels and piping still occur, particularly in Canada (Journey Energy pipeline, Edmonton, 2017) and the United States (ExxonMobil refinery, Baton Rouge, 2016). The storage of a fluid under pressure can represent a seri-ous risk of dangerousness, not only to the employees, but also to the emergency services, the population in the vicinity and the environment. Currently, technical aspects are the main concern of the regulatory author-ities (TSSA O. Reg. 220/01, RBQ B-1.1, r. 6.1, US National Board of Boiler and Pressure Vessel Inspectors (NBBI)) and the scientific community with a particular focus on risk assessment related to structural integrity and leak tightness. The present paper explores the non-compliance of stand-ards (API and ASME) in 50 accidents cases that occurred in Canada and the United States from 1997 to 2017 and related to pressure vessels and piping in the petrochemical and nuclear industry. Moreover, it also pre-sents an analysis of these accidents using a risk ranking network and Venn diagram. The analysis indicates that the main cause of two-thirds of the documented accidents is an organisational issue that includes non-compliance with standards, health and safety management violation or its absence, training deficiency, non-compliance with work procedures, and absence of clear and detailed maintenance procedures. However, in most cases, if a good safety management and clearer operation procedures ex-isted and were respected the majority of the accidents could have been prevented. From this standpoint, the Systems-Theoretic Accident Model and Processes (STAMP) method is used to analyse the causes of one particular accident as a case study.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".