The contribution of enzymes and process chemicals to the life cycle of ethanol
Bibliographic record
Abstract
Most life cycle studies of biofuels have not examined the impact of process chemicals and enzymes, both necessary inputs to biochemical production and which vary depending upon the technology platform (feedstock, pretreatment and hydrolysis system). We examine whether this omission is warranted for sugar-platform technologies. We develop life cycle ('well-to-tank') case studies for a corn dry-mill and for one 'mature' and two near-term lignocellulosic ethanol technologies. Process chemical and enzyme inputs contribute only 3% of fossil energy use and greenhouse gas (GHG) emissions for corn ethanol. Assuming considerable improvement compared to current enzyme performance, the inputs for the near-term lignocellulosic technologies studied are found to be responsible for 30%–40% of fossil energy use and 30%–35% of GHG emissions, not an insignificant fraction given that these models represent technology developers' n th plant performance. Mature technologies which assume lower chemical and enzyme loadings, high enzyme specific activity and on-site production utilizing renewable energy would significantly improve performance. Although the lignocellulosic technologies modeled offer benefits over today's corn ethanol through reducing life cycle fossil energy demand and GHG emissions by factors of three and six, achieving those performance levels requires continued research into and development of the manufacture of low dose, high specific activity enzyme systems. Realizing the benefits of low carbon fuels through biological conversion will otherwise not be possible. Tracking the technological performance of process conversion materials remains an important step in measuring the life cycle performance of biofuels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".