Retrofit Design of Hydrogen Network in Refineries: Mathematical Model and Global Optimization
Bibliographic record
Abstract
The problem of retrofit design of refinery hydrogen networks is addressed in this work, using the mathematical superstructure optimization. The superstructure of retrofit hydrogen network design contains hydrogen using, producing, and purifying units; along with compressors to facilitate hydrogen distribution. The developed mathematical model is formulated as a mixed integer nonlinear programming model (MINLP), with the objective being minimum total annual cost. The nonlinearity in the model is because of the bilinear, posynomia, and linear fractional terms. A new heuristic method is presented which helps in assigning suction and discharge pressures for the newly retrofitted compressor. With such an assignment, the nonlinearity in the model is now only confined to bilinear terms. This bilinear MINLP model is solved to global optimality using the proposed global optimization algorithm. Tests on some literature examples show that the proposed algorithm can reach global solutions faster than some commercial MINLP global solvers.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".