Thermal Crackability/Processability of Oils and Residua
Bibliographic record
Abstract
The maximum potential for producing light ends under a set of operational parameters for oil feedstocks is termed “crackability”. When constrained by the appearance of undesirable solid phases, the term “processability” applies to the potential of light ends production. These parameters were correlated with the thermal maturity of the samples. Thermal maturity was inferred from two proposed indicators: the first one was based on the “delta solubility parameter” (ΔPS) and the second was based on differential heavy hydrocarbon abundances determined by high-temperature simulated distillation (Δ%(C44–C100)). A set of 36 samples that was comprised of oils, distillation residua, and thermally cracked residua was studied. Samples with increased thermal maturity were found to decrease their crackability/processability. Samples with high solubility parameter (ΔPS), low Δ%(C44–C100), and low molecular weight were found to have increased thermal maturity. Attempts to correlate saturates, aromatics, resins, and asphaltenes (SARA) group-type distributions with thermal maturity were not successful. Highly paraffinic samples were found to deviate from the determined behavior of studied samples. Application of the proposed crackability/processability indicators to thermal processing under batch, semi-batch, and continuous setup conditions, was found to describe most of the experimentally determined results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".