Assessment of the greenhouse gas emission footprint of a biorefinery over its life cycle
Bibliographic record
Abstract
Expanding the product portfolio of a biorefinery has the potential to improve the economics of the biorefinery as it not only increases revenue but also improves valuable feedstock use. Such process improvement, however, results in added complexity, energy consumption, and emissions. This study evaluated the energy consumption and greenhouse gas (GHG) emissions of an integrated multi-product biorefinery from a life cycle perspective. Six pathways were assessed in which the by-products of fast pyrolysis – biochar and non-condensable gases (NCGs) – were upgraded to produce ethanol and hydrogen, in addition to bio-oil. The six pathways include six corresponding biorefinery configurations. The configurations differ by NCG application and the kind of fuel used to supplement process heat demand. The GHG emissions intensity of the assessed pathways is between 13.54 and 43.13 gCO 2 eq/MJ. Our assessment shows a higher GHG emissions intensity in the assessed pathways than the base pathway, in which only bio-oil is produced. Generally, the emission intensities of biorefinery products are lower than when these products are produced from fossil sources but higher than when produced from dedicated bioenergy technologies. Also, when the products are put into an end-use application, like power generation, bio-oil shows lower life cycle GHG emissions compared to conventional fossil-based power plants. When the transportation of the products to the power plant is considered, the life cycle GHG emissions of hydrogen are higher than from the conventional generation methods. Sensitivity analyses show that reducing the feedstock moisture content and increasing ethanol titer can provide significant emission reduction potential. Outside the boundaries of the biorefineries, feedstock transportation also has an impact on the overall emissions.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".