Valorization of crude glycerol from the biodiesel industry to 1,3‐propanediol by <i>Clostridium butyricum</i> DSM 10702: Influence of pretreatment with ion exchange resins
Bibliographic record
Abstract
1,3‐propanediol (1,3‐PD) was biosynthesised with Clostridium butyricum DSM 10702 using crude glycerol as a substrate. The batch fermentation of crude glycerol (70 g/L) without previous purification produced a significant concentration of 1,3‐propanediol (35.9 g/L, Y1,3‐PD/S = 0.51 g 1,3‐PD/g glycerol) at 37 °C and pH 6.5. The maximum tolerance of C. butyricum DSM 10702 to the inhibitory effect of the substrate was 70 g/L. In fed‐batch fermentation, the 1,3‐PD concentration was similar: 36.1 g/L. Pretreatment processes for biodiesel‐based crude glycerol based on ion exchange resins (Lewatit S8528 and Lewatit GF series) were evaluated. For the two different fermentation modes studied (batch and fed‐batch systems), the pretreatment slightly improved the results reached in fermentations carried out with untreated crude glycerol, especially 1,3‐PD productivity which increased from 0.52 g/(L · h) for crude glycerol to 0.99 g/(L · h) for pretreated glycerol. In batch fermentation of glycerol pretreated with a Lewatit S8528, a concentration of 41.4 g/L of 1,3‐PD was reached, similar to that found in fed‐batch fermentation.
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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) | 0.000 | 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) | 0.001 | 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".