Exploration of graphitic carbon from crude oil vacuum residue
Bibliographic record
Abstract
Preparation of graphitic carbon from low value refinery waste has captivated immense interest in past years owing to its low cost and abundant nature. Successful utilization of petroleum vacuum residue is a major challenge in the petroleum industry. In this study pyrolysis of vacuum residue fractions has been carried out for the preparation of graphitic carbon like material. The vacuum residue fractions were obtained from three different crude oils originated from Middle East, Canada and South America. The purity of the Aromatic, Resin and Asphaltene (ARA) fractions were confirmed using TLC-FID which denoted complete separation. The chemical composition were determined using elemental analysis and it revealed ARA fractions to be carbon rich regardless of its origin. Further, sulphur content was found to be high in ARA fractions from Heavy Crude oil (HCO). The degree of polymerization and molecular weight measured using GPC specify that asphaltene has high accumulation with high molecular weight compared with aromatic and resins. ARA derived carbon and heat-treated materials were analysed by TGA, XRD and Raman spectroscopy to study microstructural changes in formation of graphite like material. Thermogravimetric analysis of all ARA samples revealed the different decomposition stages for pyrolyzed, calcined and graphitized samples. The results of XRD demonstrated that the distance between the planes (d-spacing) is above 3.35 Å for all high temperature treated ARA derived carbon materials irrespective of its origin, indicating formation of graphite like structure. In Raman analysis, the nature and intensity of G and D bands evolution during each step of pyrolysis is supporting XRD results for formation of highly ordered graphitic carbon material. Furthermore, understanding feed quality has direct influence on high efficiency, low costs and sustainability, the major issues for oil refinery business.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".