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Record W3024926839 · doi:10.1071/aj19199

Impact of downward releases on the risk profile in hydrocarbon process plants

2020· article· en· W3024926839 on OpenAlexaff
Fariba Askari, Colin Crowley, Hojat Kord

Bibliographic record

VenueThe APPEA Journal · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsRisk analysis (engineering)Environmental scienceSensitivity (control systems)Process (computing)HazardJet (fluid)Risk assessmentComputer scienceEngineeringBusiness

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.083
GPT teacher head0.374
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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