Bibliographic record
Abstract
Fluid coking is a thermal conversion process that uses a conventional two-vessel circulating fluidized bed to convert heavy hydrocarbon feeds to lighter products. The technology was developed in the 1950s and since then has been used commercially around the world to upgrade heavy oils from various sources. Research work has been summarized related to all aspects of the fluid coking process, including reaction fundamentals, bed hydrodynamics, liquid distribution and jet–bed interaction, mixing of solid particles and agglomerates, mixing of vapors, control of the particle size, cleaning of the vapor stream in the scrubber, cleaning of the cold coke in the stripper, process monitoring, coke transfer lines, and the burner. The fluid coking process involves complex interactions between fluidized bed hydrodynamics, liquid feed injection, and reaction kinetics, and research tools that can take into account all of these interacting variables are requited to test methods to optimize the process. Fluid cokers can process many different types of feeds, and future applications may include blending and co-processing a variety of feedstocks ranging from waste plastics, pyrolytic bio-oil, and off-spec vegetable oils with heavy oil. The findings from this summary are also relevant for other applications that inject liquid into fluidized beds, such as the fluid catalytic cracking process, olefin polymerization cooled by liquid injection, granulators, and coaters.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".