Online measurement of viscosity for biological systems in stirred tank bioreactors
Bibliographic record
Abstract
One of the most critical parameters in chemical and biochemical processes is the viscosity of the medium. Its impact on mixing, as well as on mass and energy transfer is substantial. An increase of viscosity with reaction time can be caused by the formation of biopolymers like xanthan or by filamentous growth of microorganisms. In either case the properties of fermentation broth are changing and frequently non-Newtonian behavior are observed, resulting in major challenges for the measurement and control of mixing and mass transfer. This study demonstrates a method for the online determination of the viscosity inside a stirred tank reactor. The presented method is based on online measurement of heat transfer capacity from the bulk medium to the jacket of the reactor. To prove the feasibility of the method, fermentations with the xanthan producing bacterium Xanthomonas campestris pv. campestris B100 as model system were performed. Excellent correlation between offline measured apparent viscosity and online determined heat transfer capacity were found. The developed tool should be applicable to any other process with formation of biopolymers and filamentous growth. Biotechnol. Bioeng. 2017;114: 990-997. © 2016 Wiley Periodicals, Inc.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".