Examination of different pretreatment and saccharification methods for biobutanol production in SSF process
Bibliographic record
Abstract
The objective of this thesis was to examine different pretreatment and saccharification processes of the agriculture residue (i.e. wheat straw) for enhanced production of biobutanol. The purpose was to define the best conditions to obtain maximum sugar yield during the saccharification and butanol yield during the simultaneous saccharification and fermentation (SSF). Three different pretreatment methods for the wheat straws were examined in the present work in comparison with no chemical pretreatment as a reference. This included water, acidic, and alkaline pretreatment. For all cases, physical pretreatment represented by 1 mm size reduction of the straws was applied prior to each pretreatment. Results showed that 16.91 g/L glucose concentration and 100% glucose yield were produced from saccarification with just the physical pretreatment (i.e. no chemical pretreatment). This represented ~5-20% lower sugar release in saccarification compared to the other three pretreatment processes. Saccharification with acid pretreatment obtained the highest sugar concentrations, which were 18.77 g/L glucose and 12.19 g/L xylose. Water pretreatment with SSF was compared with SSF alone (i,e. no chemical pretreatment with SSF). Both processes converted more that 10% of wheat straw into butanol product. This was 2% higher that previous studies. The results illustrated that SSF with no chemical pretreatment obtained 2.61 g/L butanol. Kinetic model was developed for both processes to determine concentration profile of butanol. The SSF with no chemical pretreatment obtained 1.21% root mean square error in comparison with the kinetic model. Similarly, SSF with water pretreatment obtained 1.21% root mean square error in comparison with the kinetic model. Similarly, SSF with water pretreatment obtained 0.83% root mean square error.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".