The FMEA-Risk Analysis of Oil and Gas Process Facilities with Hazard Assessment Based on Fuzzy Logic
Bibliographic record
Abstract
The paper considers the practical application of the Failure Mode and Effect Analysis method to assess the operational reliability of the oil refineries' equipment, which is a pressing problem for the oil-producing regions and countries. Oil refineries are hazardous industries, and therefore the construction, adaptation and testing of effective risk analysis methods is an important task. The solution to this problem provides the basis for corrective management action to reduce the probability of damage from accidents to humans and the environment. The method is based on the detection probability of inconsistencies and involves elaborate ways to increase the reliability and security through risk analysis method. The approbation is performed for the Failure Mode and Effect Analysis method to assess the reliability based on the detection of defects typical to oil and gas facilities. The basic steps of the Failure Mode and Effect Analysis method are provided and show the possible options for scaling required to obtain quantitative risk assessments. The result was the quantitative risk assessment for oil transportation facilities. The supporting method for quantifying risk in emergency situations on the equipment are encouraged to use a fuzzy logic approach. The paper describes the main steps of this approach shows its applicability and the possibility of formation for quantitative estimates of the hazards of various defects in the equipment.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".