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Record W4294676841 · doi:10.1016/j.rineng.2022.100629

The flash point of elemental sulfur: Effect of heating rates, hydrogen sulfide, and hydrocarbons

2022· article· en· W4294676841 on OpenAlexaff
Connor E. Deering, M. Madekufamba, Kevin L. Lesage, Robert A. Marriott

Bibliographic record

VenueResults in Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicCombustion and Detonation Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFlash pointHydrogen sulfideSulfurFlash (photography)ChemistryHydrogenSulfideAnalytical Chemistry (journal)Materials scienceInorganic chemistryEnvironmental chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Facilities for the storage and handling of liquid sulfur are designed to avoid fires to prevent (a) deflagration, (b) breaching of hot-liquid and (c) the emission of highly toxic sulfur dioxide. To aid design and operation, current literature reports a wide range of flash points from 160 to 207 °C. This work aims to help clarify why this wide variance in measurements exists and which values are more applicable for safety and vessel integrity. The effects of heating rate, dissolved hydrogen sulfide, and hydrocarbon impurities on the apparent flash point were studied using a Pensky-Martens closed cup flash point apparatus following the standardized procedure outlined in American Society for Testing and Materials test method D93-20. The heating rate significantly affected the sulfur flash point, where slower rates yielded consistent flash points of 207 ± 3 °C. Flash points as low as 167 °C were observed when using faster rates, including the American Society for Testing and Materials recommended heating rates. Freely dissolved hydrogen sulfide lowered the flash point to 175 °C for concentrations greater than 100 ppmw, irrespective of the amount of dissolved polysulfanes. No effect on the flash point was observed for decane or hexadecane impurities up to 333 and 512 ppmw, respectively.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.223
Threshold uncertainty score0.377

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.004
GPT teacher head0.206
Teacher spread0.203 · 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 designSimulation or modeling
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

Citations3
Published2022
Admission routes1
Has abstractyes

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