Optimization of Yeast, Sugar and Nutrient Concentrations for High Ethanol Production Rate Using Industrial Sugar Beet Molasses and Response Surface Methodology
Bibliographic record
Abstract
Among the various agro-industrial by-products, sugar beet molasses produced by sugar refineries appear as a potential feedstock for ethanol production through yeast fermentation. A response surface methodology (RSM) was developed to better understand the effect of three process parameters (concentration of nutrient, yeast and initial sugar) on the ethanol productivity using diluted sugar beet molasses and Saccharomyces cerevisiae yeast. The first set of experiments performed at lab-scale indicated that the addition of 4 g/L of nutrient combined with a minimum of 0.2 g/L of yeast as well as a sugar concentration lower than 225 g/L was required to achieve high ethanol productivities (>15 g/L/d). The optimization allowed to considerably reduce the amount of yeast initially introduced in the fermentation substrate while still maximizing both ethanol productivity and yield process responses. Finally, scale-up assays were carried out in 7.5 and 100 L bioreactors using the optimal conditions: 150 g/L of initial sugar concentration, 0.27 g/L of yeast and 4 g/L of nutrient. Within 48 h of incubation, up to 65 g/L of ethanol were produced for both scales, corresponding to an average ethanol yield and sugar utilization rate of 82% and 85%, respectively. The results obtained in this study highlight the use of sugar beet molasses as a low-cost food residue for the sustainable production of bioethanol.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".