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Record W4289201603 · doi:10.1002/cjce.24585

Challenges of oxidative/extractive desulphurization of heavy fuel oil

2022· article· en· W4289201603 on OpenAlexvenueno aff
Ahmad Mortezaee, Salman Movahedirad, Mohammad Amin Sobati

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsnot available
FundersIran National Science Foundation
KeywordsDibenzothiopheneChemistryHydrogen peroxideFuel oilSulfurFlue-gas desulfurizationDimethylformamideExtraction (chemistry)SolventPeroxideChemical engineeringOrganic chemistryWaste management

Abstract

fetched live from OpenAlex

Abstract Oxidative desulphurization (ODS) of heavy fuel oil (HFO) has some challenges such as gum formation and a high level of waste hydrocarbons. Simple calculations show that, assuming dibenzothiophene (DBT) as a representative component of sulphur‐containing components in the cut, about 20% of hydrocarbons are lost within the extraction process. An experimental investigation has been conducted in a three‐neck glass flask with a mechanical stirrer to obtain more insight about the gum formation during ODS of HFO. The gum formation process was investigated in diluted forms of fuel oil. It was observed that the fuel oil converted to the gum increases by decreasing the hydrogen/carbon (H/C) content of the diluting solvents. The main reason for gum formation during ODS is some polymer formation reactions induced by peroxide radicals. At an oxidant to sulphur ratio (O/S) ratio of 0.25, no gum was formed, while at O/S = 5.0, all of the fuel oil was converted to gum. Finally, a simple and efficient extractive desulphurization procedure has been proposed as an alternative method. At the best condition, about 84% desulphurization was obtained with dimethylformamide (DMF) solvent extraction of HFO. It can be assumed as a potential method to use mild mixing conditions with low contact time for extractive desulphurization of HFO on an industrial scale.

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)0.000
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.705
Threshold uncertainty score0.322

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.197
Teacher spread0.182 · 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 designBench or experimental
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

Citations6
Published2022
Admission routes1
Has abstractyes

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