Estimation of life cycle greenhouse gas emissions of asphaltene-based carbon fibers derived from oil sands bitumen
Bibliographic record
Abstract
Bitumen-derived asphaltenes have emerged as a potential precursor for carbon fiber production. The technology to make it possible is in the early stages of research and development and so there are no studies on the GHG emissions from the process, which are critical to compare its carbon footprint with that of polyacrylonitrile (PAN), the standard precursor for carbon fiber production. The asphaltene-based carbon fiber (ACF) life cycle stages are bitumen production, asphaltene separation, precursor manufacturing, and carbon fiber production. In this study, we developed data-intensive models to estimate the life cycle GHG emissions of ACF. Sensitivity and uncertainty analyses were also performed to determine the inputs that have the largest effect on the GHG emissions of ACF. The results show that the life cycle GHG emissions of ACF are 16.2 kg CO2eq/kg CF. This value represents a reduction of 68.7% compared to PAN-based carbon fiber. The carbon fiber production stage is the most emission-intensive, mainly because of the high consumption of electricity. When uncertainty is considered, the life cycle GHG emissions of ACF range from 10.4 to 21.8 kg CO2eq/kg CF. The outcome of this study confirms the potential of asphaltenes as a low-carbon intense raw material for carbon fiber production.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".