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Evaluation of the Physical-Chemical Properties in Petroleum Coke

2013· article· en· W1873953190 on OpenAlexvenueno aff
Semaghiul Birghilă, Ionela Carazeanu Popovici

Bibliographic record

VenueAdvances in petroleum exploration and development · 2013
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum cokeCokeRaw materialMoistureChemistrySulfurCalcinationMetallurgyWater contentOrganic matterWaste managementMaterials scienceOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

In this paper, commercial petroleum cokes were analyzed, before and after heat treatment in order to evaluate the physical-chemical properties. The content of moisture, volatile matter, sulphur, ash and metals (Fe Ni, Cr) was determined. The results showed that temperature increasing led to the removal of volatile matter and sulphur content; higher separation of sulphur was observed for coke obtained from atmospheric residue and from decanted oil. The ash content indicates the presence of inorganic admixtures in coke. Metal concentrations increased through calcining, due to the weight loss from removal of the volatile matter; the coke samples revealed the higher content of nickel (151 to 279 ppm) which is the most represented metal component in the coking feedstock. Physical-chemical parameters indicate that the analyzed cokes can be used in anode –grade production. Key words: Anode materials; Petroleum coke; Moisture; Sulphur; Volatile matter; Metals

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.031
GPT teacher head0.270
Teacher spread0.239 · 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 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

Citations3
Published2013
Admission routes1
Has abstractyes

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