Integration of hydrogen management in refinery planning with rigorous process models and product quality specifications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
New trends of increased heavy crude markets and clean-fuel legislation, to produce ultra low-sulphur (ULS) gasoline and diesel fuels, are forcing refineries to increase their consumption of hydrogen. This critical situation raises the need to have a tool for operating refineries with flexibility and profitability. This paper addresses the planning of refinery with consideration to hydrogen availability. A systematic method for integrating a hydrogen management strategy within a rigorous refinery planning model is undertaken. The presented model consists of two main building blocks: a set of non-linear processing units’ models and a hydrogen balance framework. The two blocks are integrated to produce a refinery-wide planning model with hydrogen management. The hydrogen management alternatives were determined by economic analysis. The proposed model improves the hidden hydrogen unavailability that prevents refineries from achieving their maximum production and profit. The model is illustrated on representative case studies and the results are discussed. It was found that an additional annual profit equivalent to $7 million could be achieved with a one-time investment of $13 million in a new purification unit.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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