Multisite Refinery and Petrochemical Network Design: Optimal Integration and Coordination
Bibliographic record
Abstract
This paper addresses the design of optimal integration and coordination of a refinery and petrochemical network to satisfy given chemical products demand. The refinery and petrochemical systems were modeled as a mixed-integer problem with the objective of minimizing the annualized cost over a given time horizon among the refineries and maximizing the added value of the petrochemical network. The main feature of the paper is the development of a methodology for simultaneous analysis of process network integration within a multisite refinery and petrochemical system. This approach provides appropriate planning across the petroleum refining and petrochemical industry and achieves an optimal production strategy by allowing appropriate trade-offs between the refinery and the downstream petrochemical markets. The performance of the proposed model was tested on industrial-scale examples of multiple refineries and a poly(vinyl chloride) (PVC) complex to illustrate the economic potential and trade-offs involved in the optimization of the network. The proposed methodology not only devises the integration network in the refineries and synthesizes the petrochemical industry, but also provides refinery expansion requirements, production levels, and blending levels. The use of mathematical programming on an enterprise-wide scale to address strategic decisions considering various process integration alternatives yields substantial benefits. These benefits of process integration materialize in terms of economic considerations and improvements in the understanding of the process interactions and systems limitations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".