Integration and coordination of multisite refinery and petrochemical networks under uncertainty
Why this work is in the frame
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Bibliographic record
Abstract
In this paper we extend upon our previous work on the deterministic design of an optimal network for the integration of multisite refinery and petrochemical systems to incorporate uncertainty. A two-stage stochastic mixed-integer program with the objective of minimising the refining cost over a given time horizon and maximising the added value by the petrochemical network is proposed in this paper. Uncertainties considered in this study are in terms of imported crude oil price, refinery product price, petrochemical product price, refinery market demand, and petrochemical lower level product demand. The proposed method adopts the sample average approximation (SAA) method for scenario generation and optimal gab statistical bounding. The model performance is tested on an industrial case study of multiple refineries and a polyvinyl chloride (PVC) complex.
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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