Frequency Failure Causes Analysis of Pressure Vessel and Piping Equipment: Case Study of the Alberta Petrochemical Industry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In recent decades, many accidents involving pressure vessels and piping components (PVP) have occurred in North America. Several studies have been conducted to understand their causes and find suitable solutions to reduce their frequency. Most of the researches have focused on the technical causes of these accidents and the subsequent implementation of safety management strategies. These researches highlight that the main technical causes are leak and rupture. From this standpoint, it is important to deepen the study of these causes to better understand the risk of accident in PVP applications. In Alberta alone, the Alberta Energy Regulator (AER) showed that more than 15 root causes initiated leak and rupture failures in PVP since 1990. This paper presents an analysis of the frequency of accidents, their severity, their causes, and the risk associated in the Alberta petrochemical industry from 2008 to 2017. This study proposes an exponential decay function to estimate the frequency of accidents involving PVP and identifies the most important causes based on a severity analysis. The results based on the frequency model show that there is a good agreement between the predicted and observed accidents frequency from 2008 to 2017. The severity analysis results shows that the main factors contributing to accidents are corrosion, construction deficiency, and overpressure. Finally, the proposed model of frequency and severity of observed and predicted PVP failures, is a useful tool for risk assessment and prevention program implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it