Process optimization of multi-feed SSCF
Bibliographic record
Abstract
Economical production of bio-ethanol from lignocellulosic materials requires an efficient and robust process which enables high-solid fermentation of pretreated lignocellulose to achieve high ethanol fermentation performance. In this work, we design and optimize a high-solid fed-batch simultaneous saccharification and co-fermentation (SSCF) process with a feed of substrate, enzyme and yeast cell for efficient production of ethanol from pretreated wheat straw in both lab and pilot scale. The yeast is prepared by pre-cultivation and adaptation in a semi-continuous cultivation in liquid hydrolysate medium in order to achieve high fermentation capacity. The feeding profiles in both pre-cultivation and SSCF steps are optimized based on a previously developed multi-feed SSCF model [1] in order to maintain high activities of both hydrolytic enzyme and yeast cell resulting in highest biomass yield during pre-cultivation and highest ethanol production efficiency during SSCF process. We also demonstrate scale up of fed-batch SSCF process in a 10 m3 pilot-scale bioreactor. The fed-batch SSCF with an optimized feed of substrate, cell and enzymes reaches high ethanol fermentation performance suggesting it to be a promising process for efficient bioconversion of lignocellulosic materials to ethanol.[1] Wang et al. Bioresour. Technol., 2014
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".