Biomass to ethanol pathways: Evaluation of lignocellulosic ethanol production technologies
Bibliographic record
Abstract
Ethanol derived from lignocellulose (biomass) is a promising alternative to petroleum-based fuels for light-duty vehicle transportation; in particular, because it represents a near-term liquid fuel that is renewable, domestically produced, requires moderate infrastructure changes, and has the potential to significantly reduce greenhouse gas emissions. This thesis investigates environmental and energy impacts of a set of emerging lignocellulosic ethanol technologies using life cycle assessment and statistical methods to characterize the potential performance and development of those technologies. A set of emerging pretreatment methods are investigated in combination with near-term saccharification and co-fermentation and mature consolidated bioprocessing technologies for producing ethanol from lignocellulose. Results from the research show that relative to gasoline vehicles, lignocelulose-based ethanol fuelled vehicles in blends with 15% reformulated gasoline (by volume) can reduce greenhouse gas emissions by between 25% and 130% per kilometre driven depending upon the feedstock and conversion technology selected. Furthermore, the analysis shows the potential for displacing between 9% and 40% of U.S. annual gasoline demand, and more than 100% of Canadian annual gasoline demand over the next 20 years. A major finding is the impact of co-product allocation: when electricity co-product credits are incorporated into model designs, technologies with low ethanol yield and high electricity yield are preferred from an emissions and energy perspective. However, this finding depends strongly on whether the electricity co-product is displacing mostly coal-based existing electricity facilities, and it is likely that producing larger amounts of ethanol fuels to displace gasoline will help reduce fossil and petroleum energy, and greenhouse gas emissions from light-duty vehicle transportation, as well as contribute to energy security goals for the sector. This thesis demonstrates how systems-based analytical models that measure the full set of environmental tradeoffs associated with emerging fuel technologies are essential inputs to guide decision making and policies for the next generation of transportation energy infrastructure.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".