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Record W3136148119 · doi:10.15826/rjcst.2019.2.002

ANALYZING DOMINO EFFECTS OCCURRING ON GASOLINE STORAGE TANKS AT THE BULK OIL STORAGE AND TRANSPORTATION (BOST) DEPOT

2019· article· en· W3136148119 on OpenAlexaff
G. A. K. MacCarthy, C. N. Asamoah, B. A. Ephraim, В. Н. Алехин, L. V. Poluyan

Bibliographic record

VenueRussian Journal of Construction Science and Technology · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsMcGill University
Fundersnot available
KeywordsFlammable liquidGasolineDomino effectEnvironmental scienceHazardous wasteFlammability limitWaste managementEnvironmental engineeringStorage tankEngineering

Abstract

fetched live from OpenAlex

Since processed crude oil products are very vulnerable (susceptible) and highly flammable to cause massive catastrophes, such as fire and explosion, which are frequent and can create a chain reaction (Domino effects). This research was carried out at the Bulk Oil Storage and Transportation LTD depot on the Accra plain in Ghana where gasoline and Gasoil are stored. The research was conducted on a flammable gasoline area subjected to a vapor cloud explosion and the hazardous zone. Analyzing domino effects, propagation of a gasoline flammable vapor cloud caused by the explosion, ALOHA (Areal Location of Hazardous Atmospheres) software was used to find out how to apply effective safety measures to prevent future risks at any BOST facilities across the country. After the analysis, it was realized that 5.0 miles to the west-south-west the fuel concentration in the air was 2100 ppm lower than the explosive limit (LEL), and could not be as severe as that at 2.3 miles distance from the source point (12600 ppm LEL) with a greater fuel concentration in the atmosphere. The results made available would be useful in maximizing (improving) safety at the facility, residential area, and as well as minimizing future incidents.

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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.781
Threshold uncertainty score0.569

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.002
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.286
Teacher spread0.275 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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