Optimal synthesis of a heat‐integrated petroleum refinery configuration
Bibliographic record
Abstract
The conceptual design of a petroleum refinery that satisfies multiple economics and operating constraints is a highly complex task. Coupled with the ever‐rising cost of designing and constructing a new refinery and the increasing demand for energy and fuels, there is incentive to optimize the energy recovery and energy efficiency of such a facility. This work addresses the flowsheet optimization of the synthesis of a petroleum refinery to attain an optimal heat‐integrated configuration or topology. A sequential two‐step strategy is employed that first performs simultaneous flowsheet optimization and heat integration to obtain an optimal refinery topology with minimum utility cost. Subsequently, the fixed optimal topology with minimum utility loads is optimized to arrive at a configuration with the fewest heat exchanger units. A mixed‐integer linear program (MILP) is formulated based on a superstructure representation that considers many alternative feasible refinery topologies. The computational results show meaningful reduction in the total annualized capital and operating costs as compared to a non‐heat‐integrated configuration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".