Food Grade Ethanol Production Process of Sorghum Stem Juice Using Immobilized Cells Technique
Bibliographic record
Abstract
One form of economic development efforts for waste utilization in rural communities is to utilize stem sorghum to produce food grade ethanol. Sorghum stem juice with 150 g/L of sugar concentration was fermented using conventional batch process and cell immobilization continuous process with K-carrageenan as a supporting matrix. The microorganism used was Mutated Zymomonas Mobilis to be compared with a mixture of Saccharomyces Cerevisiae and Pichia Stipitis, and a mixture of Mutated Zymomonas Mobilis and Pichia Stipitis. Ethanol in the broth, result of fermentation process, was separated in packed distillation column. Distilate of the column, still contain water and other impurities, was flown into molecular sieve for dehydration and activated carbon adsorption column to remove the other impurities to meet food grade ethanol specification. The packing used in distillation process was steel wool. For batch fermentation, the fermentation using a combination of Saccharomyces Cerevisiae and Pichia Stipitis produced the best ethanol with 12.07% of concentration, where the yield and the productivity were 63.49%, and 1.06 g/L.h, respectively. And for continuous fermentation, the best ethanol with 9.02% of concentration, where the yield and the productivity were 47.42% and 174.27 g/L.h, respectively, is obtained from fermentation using a combination of Saccharomyces Cerevisiae and Pichia Stipitis also. Fermentation using combination microorganism of Saccharomyces Cerevisiae and Pichia Stipitis produced higher concentration of ethanol, yield, and productivity than other microorganisms. Distillation, molecular sieve dehydration and adsorption process is quite successful in generating sufficient levels of ethanol with relatively low amount of impurities.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".