Testing and analysis of renewable/bio/conventional diesel blends for marine vessel applications: task 2 report
Bibliographic record
Abstract
Renewable diesel (RD) and biodiesel (BD) are produced from renewable resources. Replacing petroleum diesel by RD and/or BD in power generation has the potential to reduce life-cycle greenhouse gas (GHG) emissions. Therefore, the Canadian Coast Guard (CCG) has been developing a strategy to introduce RD, BD or their blends into its small and large vessel fleet to help reduce net GHG emissions. During this process, immediate questions that need to be addressed are what the optimal RD/BD blend ought to be, how much of the RD/BD blend can be introduced, and how these blends will affect the combustion and emissions performance of the engines powering the vessels. In this project, the National Research Council (NRC) and CCG work together to characterize and optimize the properties of RD, BD and their blends with petroleum-based ultra-low-sulfur diesel (ULSD), as well as evaluate the combustion and emissions performance of RD/BD/ULSD blends when applied to heavy-duty diesel engines. This interim report summarizes the findings from Task 2 of the project, which was designed to characterize properties of RD/BD blends at five different blending ratios. The findings reveal that although the BD volume percentage in a RD/BD blend causes the variation in fuel properties, most properties of the tested RD/BD blends meet the Canadian General Fuel Standard specifications except for cloud point and density. However, none of the five tested RD/BD blends meets the cloud point specification for type 15 (winter) marine diesel, suggesting that cold temperature performance is a challenge for the applications of the tested RD, BD, and their blends. All tested RD/BD blends meet the Canadian General Fuel Standard density specification for type 11 (summer) marine diesel, but the density does not meet the standard specification for type 15 marine diesel when BD volume percentage is lower than 20% in a RD/BD blend. There is a possibility that the RD and BD tested are not “winter” (i.e. type 15) diesel, since they were obtained from Province of British Columbia.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".