Bibliographic record
Abstract
Gasoline is produced by blending several different components in ratios such that the blended mixture meets the required quality specifications. The blender produces different batches of gasoline by switching operation from one grade of gasoline to another. Blend planning horizon usually spans 10 to 14 days. Blend plan optimization minimizes the total blend costs by solving a multiperiod problem, where demands need to be satisfied in each period and some inventory is carried into the future time periods to meet the demands. Since blend component production is determined by a longer range refinery production plan, inventory carrying costs are not included in the objective function. It is shown that nonlinear programming (NLP) as well as mixed integer nonlinear programming (MINLP) solvers lead to different blend recipes and different blend volume patterns for the same total cost. The new algorithm described in this work systematically searches for multiple optimum solutions; this opens the way for blend planners to select from different blend plans based on additional considerations (e.g., blend more of regular gasoline earlier in the planning horizon thereby creating an opportunity to meet more demand for it in early periods) instead of having to use only one solution that varies with the choice of the solver. Inherent structure of the proposed algorithm makes it well suited for implementation on parallel CPU machines.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".