Impact of cellulase production on environmental and financial metrics for lignocellulosic ethanol
Bibliographic record
Abstract
Abstract The cost of cellulases remains a key issue in the production of cellulosic ethanol, and the impact of enzymes on greenhouse gas (GHG) emissions of cellulosic ethanol has received little attention. This study evaluates life cycle emissions and cellulase production costs for bioethanol production, considering on‐site and off‐site production options. A complete enzyme production process was simulated using AspenPlus, generating mass and energy balance information required to calculate GHG emissions and financial metrics. GHG emissions for cellulase production range from 10.2 to 16.0 g CO2 eq g–1 enzyme protein, depending on on‐site or off‐site production and the method of transportation. Enzyme GHG emissions are predicted to be 258 g CO2 eq. L–1 of ethanol for on‐site production, versus 403 g CO2 eq. L–1 for off‐site production, based on a 150 MMLY ethanol plant using 11.5 mg enzyme g–1 substrate and a cellulase fermentation yield of 90%. Cellulase production costs were estimated for a range of conditions, including ethanol plant size, enzyme dose and protein yield for on‐site production, and enzyme plant size, protein yield and return on investment for off‐site production. On‐site production costs range between $3.80 and $6.75 kg–1 protein, versus $4.00 to $8.80 kg–1 for off‐site production. In both scenarios, the lowest cost corresponds to a 90% protein yield, and a high enzyme demand and production capacity. An enzyme production cost of $4.70 USD kg–1 corresponds to an enzyme cost of 0.46 USD gal–1 ($0.12 L–1) of ethanol in a 150 MMLY plant using 11.5 mg enzyme g–1 substrate. © 2013 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".