Optimization of bioethanol production from sorghum green malt and cane molasses using <scp> <i>Saccharomyces bayanus</i> </scp> in submerged cultivation
Bibliographic record
Abstract
Abstract This study focuses on optimizing bioethanol production using Saccharomyces bayanus in a submerged culture medium containing sorghum green malt and cane molasses as mixed carbon sources. A Taguchi experimental design L 9 (3 4 ) was employed to evaluate the effects of the concentrations of cane molasses, urea, and CaCO 3 , as well as the initial pH, on bioethanol yield. The results demonstrated that molasses concentration and initial pH were the most significant factors influencing bioethanol production. The optimal treatment achieved a bioethanol concentration of 139.10 g/L after 48 h of fermentation, with a productivity of 2.90 g/(L · h) and a yield of 1.22 g of bioethanol produced per g of reducing sugars consumed. Additionally, the modified Monod model accurately described the fermentation kinetics, capturing trends in yeast growth and substrate consumption. This model is an essential tool for scaling up the bioethanol production process in continuous bioreactors. Results suggest that sorghum green malt, supplemented with cane molasses, provides a low‐cost, nutritionally complete, and environmentally friendly culture medium for bioethanol production.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".