Strategic Planning of Integrated Multirefinery Networks: A Robust Optimization Approach Based on the Degree of Conservatism
Why this work is in the frame
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Bibliographic record
Abstract
This paper considers the problem of strategic planning under uncertainty for optimal integration and coordination of a multirefinery network. The deterministic model proposed by Al-Qahtani and Elkamel [ Comput. Chem. Eng. 2008, 32, 2189−202] was extended to account for uncertainties in raw material costs and final product prices, as well as product demand. The robust optimization methodology of Bertsimas and Sim [ Op. Res. 2004, 52, 35−53] was applied, which deals with uncertainty in a tractable manner and does not add complexity to the deterministic problem. An industrial-scale study illustrated the benefits of the integrated planning and demonstrated that the modeling of uncertainty in process parameters provides a more practical perspective of the refining industry. In addition, probability bounds of constraint violation were calculated to help decision makers select appropriate parameters to control solution robustness and evaluate trade-offs between conservatism and total profit.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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