Development and Application of a Life Cycle-Based Model to Evaluate Greenhouse Gas Emissions of Oil Sands Upgrading Technologies
Bibliographic record
Abstract
A life cycle-based model, OSTUM (Oil Sands Technologies for Upgrading Model), which evaluates the energy intensity and greenhouse gas (GHG) emissions of current oil sands upgrading technologies, is developed. Upgrading converts oil sands bitumen into high quality synthetic crude oil (SCO), a refinery feedstock. OSTUM’s novel attributes include the following: the breadth of technologies and upgrading operations options that can be analyzed, energy intensity and GHG emissions being estimated at the process unit level, it not being dependent on a proprietary process simulator, and use of publicly available data. OSTUM is applied to a hypothetical, but realistic, upgrading operation based on delayed coking, the most common upgrading technology, resulting in emissions of 328 kg CO 2 e/m 3 SCO. The primary contributor to upgrading emissions (45%) is the use of natural gas for hydrogen production through steam methane reforming, followed by the use of natural gas as fuel in the rest of the process units’ heaters (39%). OSTUM’s results are in agreement with those of a process simulation model developed by CanmetENERGY, other literature, and confidential data of a commercial upgrading operation. For the application of the model, emissions are found to be most sensitive to the amount of natural gas utilized as feedstock by the steam methane reformer. OSTUM is capable of evaluating the impact of different technologies, feedstock qualities, operating conditions, and fuel mixes on upgrading emissions, and its life cycle perspective allows easy incorporation of results into well-to-wheel analyses.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".