Hinging Hyperplanes Crude Oil Mixing Model for Production Planning Optimization
Bibliographic record
Abstract
Abstract Refinery feed, usually a mixture of several crude oils, is separated via a crude distillation unit (CDU). Crude true boiling point (TBP) distillation curve determines the amount of products which can be obtained via CDU separation. The TBP curve of the mixed crude feed is calculated by blending pseudocomponents from each individual crude; the blending calculation introduces a lot of nonlinear terms and makes it difficult to optimize the crude selection and the downstream refinery operation. This work proposes a continuous piecewise linear (CPWL) model to approximate the crude blending, thereby eliminating the nonlinear blending terms. A hinging hyperplanes (HH) model is employed to formulate the CPWL model. In addition, its parameters are computed by a two-layer perceptron, which is built to simulate the HH model. This leads to a mixed-integer linear model suitable for crude oil selection and cut point optimization. The proposed model enables accurate computation of the mixed crude feed TBP curve while eliminating nonlinear blending terms. Hence, it is suitable for inclusion in refinery planning models dealing with optimal crude selection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.001 |
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".