Petroleum Residue Upgrading Via Delayed Coking: A Review
Bibliographic record
Abstract
Abstract World petroleum residue processing capacity has reached about 725 million metric tons per annum (MMTPA). The high demand for transportation fuels and the ever‐rising heavy nature of crude oil have resulted in a renewed interest in the bottom‐of‐the‐barrel processing using various conversion processes. Delayed coking, known for processing virtually any refinery stream (which not only poses a serious threat to environment, but also involves a disposal cost) has garnered tremendous importance in the current refining scenario. Needle coke obtained from delayed coking process is a highly sought‐after product, which is used in electric arc furnaces (in the form of graphite electrodes) in steel making applications. In the present communication, the published literature has been extensively analyzed and a state‐of‐the‐art review has been written that includes: (1) importance and place of delayed coking as a residue upgrading process in the current refining scenario; (2) coking mechanism and kinetics; (3) design aspects; (4) feedstocks suitable for the production of needle coke; (5) characteristics of needle coke; (6) factors affecting needle coke quality and quantity; and (7) future market for needle coke. An attempt has been made to get the above‐mentioned aspects together in a coherent theme so that the information is available at a glance and could be of significant use for researchers and practising refiners.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".