In‐line mixing for production of citric acid by<i>Candida lipolytica</i>grown on<i>n</i>‐paraffins
Bibliographic record
Abstract
Abstract This study reports on the effects of internal fermenter and external in‐line agitation and fed‐batch mode of operation on citric acid production fromCandida lipolyticausingn‐paraffin as the carbon source. An optimum range of fermenter agitation speeds in the range 800–1000 rpm corresponding to Reynolds numbers of 50433–62947 (based on initial batch conditions) seemed to give the best balance between substrate utilization for biomass growth and citric acid production. Proof of concept evidence is presented that indicates that an external in‐line agitator could be used in place of high speed internal agitation to increase citric acid production. However, more work is required to optimize the external agitator concept. Application of multiple fed‐batch feedings can be used to extend the batch fermentation and increase final citric acid concentrations and product yield. Experiments were conducted implementing a three‐cycle fed‐batch process which increased overall citric acid yields to 0.8–1.0 g citric acid g−1n‐paraffin, approximately 200% improvement from those found in the normal batch process. The three‐cycle fed‐batch mode of operation also increased the final citric acid concentration to 42 g dm−3from about 6 g dm−3for normal batch operation. Increased citric acid concentrations in three‐cycle fed‐batch mode was achieved at longer fermentation times. Copyright © 2004 Society of Chemical Industry
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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 | 0.001 | 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".