Potential Air Emission Impacts of Cellulosic Ethanol Production at Seven Demonstration Refineries in the United States
Bibliographic record
Abstract
This paper reports on the estimated potential air emissions, as found in air permits and supporting documentation, for seven of the first group of precommercial or "demonstration" cellulosic ethanol refineries (7CEDF) currently operating or planning to operate in the United States in the near future. These seven refineries are designed to produce from 330,000 to 100 million gal of ethanol per year. The overall average estimated air emission rates for criteria, hazardous, and greenhouse gas pollutants at the 7CEDF are shown here in terms of tons per year and pounds per gallon of ethanol produced. Water use rates estimated for the cellulosic ethanol refineries are also noted. The air emissions are then compared with similar estimates from a U.S. cellulosic ethanol pilot plant, a commercial Canadian cellulosic ethanol refinery, four commercial U.S. corn ethanol refineries, and U.S. petroleum refineries producing gasoline. The U.S. Environmental Protection Agency (EPA) air pollution rules that may apply to cellulosic ethanol refineries are also discussed. Using the lowest estimated emission rates from these cellulosic ethanol demonstration facilities to project air emissions, EPA's major source thresholds for criteria and hazardous air pollutants might not be exceeded by cellulosic ethanol refineries that produce as high as 25 million gal per year of ethanol (95 ML). Emissions are expected to decrease at cellulosic ethanol refineries as the process matures and becomes more commercially viable.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".