Optimal Design of Petroleum Refinery Configuration Using a Model-Based Mixed-Integer Programming Approach with Practical Approximation
Bibliographic record
Abstract
We present a model-based optimization approach to determine the configuration of a petroleum refinery for grassroots (new) or existing site that considers a large number of commercial technologies particularly for heavy oil processing of crude oil residue from an atmospheric distillation unit. First, we develop a superstructure representation for the refinery configuration to encompass all possible topology alternatives comprising 96 technologies and their interconnectivities. The superstructure is postulated by decomposing it to incorporate representative heavy oil processing scheme alternatives that center on the technologies for atmospheric residual hydrodesulfurization (ARDS), vacuum residual hydrodesulfurization (VRDS), and residual fluid catalytic cracking (RFCC). We formulate a mixed-integer linear program (MILP) based on the superstructure by devising logic propositions on design and structural specifications that represent these processing options to aid convergence to an optimal refinery configuration. A numerical example is illustrated to implement the proposed technique in which an equivalent of more than two million refinery plot plans is evaluated. To assess the applicability and value of the approach, we validate the results against the literature as well as compare with existing real-world refinery configurations. A main contribution of this work is to demonstrate how a mixed-integer programming approach can be applied to a large-scale petroleum refinery design problem with suitable approximations informed by practical considerations to obtain results with reasonable computational load.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".