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Record W2894758249 · doi:10.1515/ijcre-2017-0215

A Versatile Converter of Liquid Hydrocarbons for the Production of Reducing and Carbonization Atmospheres

2018· article· en· W2894758249 on OpenAlexfundno aff
A. Al‐Musa, Vladimir Martynenko, Mohammed Al-Saleh, Ayman Al-Zahrani, Vladimir I. Kalinin, S. I. Shabunya

Bibliographic record

VenueInternational Journal of Chemical Reactor Engineering · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysis and Oxidation Reactions
Canadian institutionsnot available
FundersKing Abdulaziz City for Science and TechnologyYork University
KeywordsNaphthaGasolineEndothermic processDiesel fuelChemistryKeroseneNatural gasRaw materialMethaneCatalysisYield (engineering)Jet fuelCarbonizationSynthetic fuelChemical engineeringSyngasWaste managementOrganic chemistryMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Abstract We herein report the results of our investigation into the modes of catalytic partial oxidation (CPOX) of liquid fuels and air mixtures to yield endothermic (endo) gas on a pilot-scale installation containing ~ 0.45 cm 3 catalytic bed. This endothermic gas serves as a protective atmosphere in thermochemical steel treatment processes. Seven liquid hydrocarbons (LHs) are investigated, namely isooctane, 91 RON (research octane number) and 95 RON gasoline, diesel, kerosene, jet fuel, and naphtha. All experiments are performed using our previously developed reactor, where the reactions of natural gas/air mixtures were previously studied. In the present study, we report that the LH conversion products reached an equilibrium state similar to that of methane and natural gas conversion with an atomic C/O ratio of ~ 1.0 in the mixture. Furthermore, working regimes between 850 and 950 °C are examined as typical reaction conditions for industrial endo gas generators, and in all cases, the required gas quality is achieved. However, we found that gasoline and diesel are the most suitable LH feedstock for endo gas generation.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.244

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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