Impact of downward releases on the risk profile in hydrocarbon process plants
Bibliographic record
Abstract
Quantitative risk assessment (QRA) calculations for major hazard installations often involve consequence analysis calculations for thousands of events, and therefore, some simplifying assumptions are generally required. The simplifications are usually designed to make the analysis reasonably practicable and reduce the cost of the QRA. Nevertheless, the overall methodology and the applied parameters should be chosen conservatively to cover possible uncertainties. One of the key assumptions in many QRAs is the release direction, which is usually assumed to be horizontal. This is generally assumed to provide a conservative representation of all other possible release directions, which may occur vertically (upward or downward) or at an angle. A sensitivity analysis has been performed and presented in this paper to investigate how different release direction assumptions affect the results of consequence analysis, and eventually, QRA outcomes, i.e. individual and societal risk results. A high-pressure hydrocarbon system is considered as a case study and SNC-Lavalin’s (formerly Atkins) in-house QRA software, ‘RiskTool’, has been used to carry out the QRA modelling. The overall conclusion is that the assumption that all releases are horizontal may lead to a significant underprediction of risks for some types of high-pressure release events. This is because an unimpeded horizontal jet may entrain air, and hence, dilute much more rapidly than a jet that impinges on the ground or nearby obstacles.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".