Comparative technoeconomic analysis of a softwood ethanol process featuring posthydrolysis sugars concentration operations and continuous fermentation with cell recycle
Bibliographic record
Abstract
Economical production of second generation ethanol from Ponderosa pine is of interest due to widespread mountain pine beetle infestation in the western United States and Canada. The conversion process is limited by low glucose and high inhibitor concentrations resulting from conventional low-solids dilute acid pretreatment and enzymatic hydrolysis. Inhibited fermentations require larger fermentors (due to reduced volumetric productivity) and low sugars lead to low ethanol titers, increasing distillation costs. In this work, multiple effect evaporation (MEE) and nanofiltration (NF) were evaluated to concentrate the hydrolysate from 30 g/l to 100, 150, or 200 g/l glucose. To ferment this high gravity, inhibitor containing stream, traditional batch fermentation was compared with continuous stirred tank fermentation (CSTF) and continuous fermentation with cell recycle (CSTF-CR). Equivalent annual operating cost (EAOC = amortized capital + yearly operating expenses) was used to compare these potential improvements for a local-scale 5 MGY ethanol production facility. Hydrolysate concentration via evaporation increased EAOC over the base process due to the capital and energy intensive nature of evaporating a very dilute sugar stream; however, concentration via NF decreased EAOC for several of the cases (by 2 to 15%). NF concentration to 100 g/l glucose with a CSTF-CR was the most economical option, reducing EAOC by $0.15 per gallon ethanol produced. Sensitivity analyses on NF options showed that EAOC improvement over the base case could still be realized for even higher solids removal requirements (up to two times higher centrifuge requirement for the best case) or decreased NF performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".