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Record W2899990348 · doi:10.1115/ipc2018-78059

Assessment of the Threat From Wildfires on Above Ground Natural Gas Facilities

2018· article· en· W2899990348 on OpenAlexaffabout
Clive G. Robinson, Zoë E. Wattis, Colin Dooley, Slađana Popović

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsPetroleum Technology Alliance Canada
Fundersnot available
KeywordsEnvironmental scienceVegetation (pathology)Block (permutation group theory)MicroclimatePipeline (software)Remote sensingComputer scienceGeologyGeography

Abstract

fetched live from OpenAlex

In the light of recent experience of wildfires in Alberta and British Columbia, Alliance Pipeline has strengthened their emergency preparedness in dealing with external fire events that have the potential to affect above-ground facilities connected with their high pressure natural gas pipeline system. As part of this initiative a quantitative methodology has been developed that enables the effects of a wildfire on an above-ground pipeline facility to be assessed. The methodology consists of three linked calculations which assess: 1. the severity of the wildfire, based on information from the Canadian Wildland Fire Information System, 2. the transmission of thermal radiation from the wildfire to the facility, and, 3. the response of equipment, structures and buildings to the incident thermal radiation. The predictions of the methodology agree well with the actual damage observed at a lateral block valve site following a wildfire in 2016. Application to example facility types (block valve sites, meter stations and compressor stations) has demonstrated that, in general, damage is only predicted for more vulnerable items such as cables. The sensitivity of the predictions of the methodology to the input parameters and key modelling uncertainties has been examined. This demonstrates that the results are sensitive to the distance of the facility from the tree line and the assumed vegetation type. This shows the importance of verifying the location relative to the vegetation and selecting the appropriate vegetation type from the Canadian Wildland Fire Information System for site specific assessments. The predictions of the methodology are particularly sensitive to the assumed flame temperature. However, a value has been chosen that gives good agreement with measured thermal radiation values from wildfires. Of the mitigation options considered, the most effective and practical is to increase the distance to the tree line. This measure has the advantage of reducing radiation levels for all items on the site. Even though the work shows that failure of exposed pipework due to wildfires is unlikely, maintaining the flow within pipes is recommended as this increases the radiative flux at which failure is predicted to occur. However, as failure of cables and hence control systems would occur at a lower flux levels the fail-safe actions of such systems needs to be confirmed. Shielding of cables or items of equipment in general is likely to be impractical but could be considered for particularly vulnerable equipment or locations.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.230
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2018
Admission routes2
Has abstractyes

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