Inventory pinch algorithm for gasoline blend planning
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Bibliographic record
Abstract
Current gasoline blend scheduling practice is to optimize blend plans via fixed duration (e.g., days) multiperiod NLP or MINLP models and schedule blends via interactive simulation. Solutions of multiperiod models typically have different blend recipes for each time period. We introduce inventory pinch points and use them to construct an algorithm based on single‐period nonlinear model to minimize the number of different blend recipes. The algorithm optimizes multigrade blend recipes for each period delimited by the inventory pinch points and then uses a fine‐grid multiperiod fixed‐recipe MILP to compute blend volumes profile. If MILP is infeasible, a corresponding period between the pinch points is subdivided and recipes are reoptimized. In our case studies, solutions are computed in significant less time and are most often within 0.01% of the solutions by multiperiod MINLP. Reduced number of blend recipes makes it easier for the blend scheduler to create a schedule by interactive simulation. © 2013 American Institute of Chemical Engineers AIChE J , 59: 3748–3766, 2013
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it