Will second‐generation ethanol be able to compete with first‐generation ethanol? Opportunities for cost reduction
Bibliographic record
Abstract
Abstract The production costs of a lignocellulosic ethanol process, both currently and projected for 2020, were compared to a corn ethanol process, to determine its economic competitiveness. A techno‐economic model was used to estimate the current production costs for a base‐case, 50 ML yr‐1 softwood facility, as well as providing a basis for cost‐reduction test cases assessing different feedstock, scaling, enzyme, and coproduct options. The progress ratio indicated that lignocellulosic ethanol could be competitive with corn ethanol by 2020, based on volumes mandated by 2007 EISA. However, cost reductions must occur across all components of the production process. The ambitious cellulase enzyme cost reductions that have been projected were shown to be challenging as cellulase costs still need to be significantly lower than those of amylase enzymes on a unit‐of‐protein basis. Opportunities for capital cost reduction relative to first‐generation plants were primarily restricted to the pre‐treatment/hydrolysis unit operations, with operational conditions such as the severity of pre‐treatment and hydrolysis residence times, significantly influencing operating costs. Alternative operating strategies, such as maximizing hydrolysis rate with shorter residence times rather than maximizing ethanol yield and using the unhydrolyzed residue for heat and power production, showed some promise. Increasing the size of the facility to 1 BL yr‐1 output substantially reduced the per unit capital costs, but not to a level competitive with an average (150 ML yr‐1) corn ethanol facility. © 2011 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".