Comparison of Methods for Computing Crude Distillation Product Properties in Production Planning and Scheduling
Bibliographic record
Abstract
Production planning and scheduling optimize feedstocks usage in refineries by using crude simplified distillation models which relate feed true boiling point (TBP) curves to product TBP curves and from these calculate product yields and properties. We compare product yield and properties calculated by the swing-cut methods (fixed-cut, weight/volume transfer, and light/heavy) with results from the pseudocuts TBP-based method. The latter uses product yields and TBP curves computed via the hybrid distillation model. Swing-cut methods use assumptions which lead to lower accuracy of predicting yields vs hybrid model yields, resulting in errors in computed bulk product properties. If swing-cut methods yields are replaced by the correct yields from hybrid model, all four methods have approximately the same accuracy. Therefore, for a mixture of crudes, by using accurate yields from the hybrid model, product bulk properties can be computed by blending rules as simple as those used by the fixed-cut swing method.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".