Life Cycle Assessment of Transportation Fuels from Canada’s Oil Sands through Development of Theoretical Engineering Models
Bibliographic record
Abstract
Oil sands in Canada are significant in fulfilling the current and the future energy demands of North America. The development of these resources, besides the increased awareness in global carbon management, has given way to various policy regulations such as the Low Carbon Fuel Standard (LCFS) and Europe’s Fuel Quality Directive that demand proper quantification and estimation of life cycle (LC) greenhouse gas (GHG) emissions from transportation fuels. Previous studies show the variability in oil sands projects and the demand for proper quantification of project-specific energy consumption and GHG emissions. The novelty of this study is its aim at developing theoretical models based on engineering first principles to quantify the energy demand and GHGs emitted in oil sands operations using project-specific parameters. These models are used to quantify the GHG emissions in surface mining, steam assisted gravity drainage (SAGD), upgrading, transportation, and refining operations, through identifying the key sensitive parameters. Further, a comprehensive life cycle assessment (LCA) for transportation fuels (gasoline, diesel, and jet fuel) derived from Canada’s oil sands is conducted in which all the possible pathways from bitumen extraction to use in vehicles are explored. The life cycle inventory data for the LCA are obtained from the developed theoretical models. The impact of cogeneration of electricity in oil sands recovery, extraction, and upgrading on the LC GHG emissions of gasoline is explored. Sub process level mass allocation is followed to allocate the refinery emissions among the products. Emissions in surface mining and SAGD range from 180 to 302 kg of CO2 eq/m3 of bitumen and 238 to 1,204 kg of CO2 eq/m3 of bitumen, respectively, representing a wide range of variability in oil sands projects. Temperature and warm water consumption in surface mining and the steam-to-oil ratio (SOR) in SAGD are major parameters affecting GHG emissions. Hydroconversion upgrading is more energy- and GHG-intensive than delayed coker upgrading but gives a higher SCO yield. Refining SCO to transportation fuels produces 41% and 49% fewer emissions than do dilbit and bitumen, respectively. LC well-to-wheel (WTW) GHG emissions range from 106.8 to 116 g-CO2eq/MJ of gasoline; 100.5 to 115.2 g-CO2eq/MJ of diesel, and 96.4 to 109.2 g-CO2 eq/MJ of jet fuel, depending on the pathway. Combustion emissions (64.7% to 70.3%) are the largest constituent of WTW emissions for gasoline production; recovery forms 7.2% to 16%. The WTW GHG intensity of pathways depends on the allocation method and transportation fuel chosen for comparison.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".