Estimating Impacts of Diesel Fuel Reformulation with Vector-based Blending
Bibliographic record
Abstract
The Oak Ridge National Laboratory Refinery Yield Model has been used to study the refining cost, investment, and operating impacts of specifications for reformulated diesel fuel (RFD) produced in refineries of the U.S. Midwest in summer of year 2010. The study evaluates different diesel fuel reformulation investment pathways. The study also determines whether there are refinery economic benefits for producing an emissions reduction RFD (with flexibility for individual property values) compared to a vehicle performance RFD (with inflexible recipe values for individual properties). Results show that refining costs are lower with early notice of requirements for RFD. While advanced desulfurization technologies (with low hydrogen consumption and little effect on cetane quality and aromatics content) reduce the cost of ultra low sulfur diesel fuel, these technologies contribute to the increased costs of a delayed notice investment pathway compared to an early notice investment pathway for diesel fuel reformulation. With challenging RFD specifications, there is little refining benefit from producing emissions reduction RFD compared to vehicle performance RFD. As specifications become tighter, processing becomes more difficult, blendstock choices become more limited, and refinery benefits vanish for emissions reduction relative to vehicle performance specifications. Conversely, the emissions reduction specifications show increasing refinery benefits over vehicle performance specifications as specifications are relaxed, and alternative processing routes and blendstocks become available. In sensitivity cases, the refinery model is also used to examine the impact of RFD specifications on the economics of using Canadian synthetic crude oil. There is a sizeable increase in synthetic crude demand as ultra low sulfur diesel fuel displaces low sulfur diesel fuel, but this demand increase would be reversed by requirements for diesel fuel reformulation.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".