Concentration of Proteins and Decrease of Salt Content in a Hydrolyzed Fermentation Broth Using Membrane Filtration
Bibliographic record
Abstract
Membrane filtration was used to concentrate proteins and decrease the salt concentration in a hydrolyzed fermentation broth. The broth was a by-product from the bacterial production and extraction of polyhydroxyalkanoates (PHA), that can be used to produce biobased plastics. 3 ultrafiltration-membranes, RC70PP, GR70PP and ETNA10PP from Alfa Laval Corporate AB, were evaluated at varying cross-flow velocities (CFV) of 0.3-0.5 m/s and transmembrane pressures (TMP) of 1-9 bar. High flux rates were measured, >100 L/m2h at CFV = 0.5 m/s and TMP 3-9, and the protein retention was >80% for RC70PP and >90% for GR70PP and ETNA10PP. Due to a limited amount of broth being available, it was necessary to dilute it before using it as feed in the study. This dilution appears to have significantly affected the filtration process, as there was a notable disparity between the results from this study and the results in the following concentration studies. Concentration studies were conducted with RC70PP and GR70PP, where the original, non-diluted broth was concentrated until a volume reduction of 90% was reached. Much lower flux rates, generally <20 L/m2h, were measured for both membranes. A high protein retention was achieved, of 97.1 % for RC70PP and 98.3 % for GR70PP. Both RC70PP and GR70PP measured fluxes around 5 L/m2h after the 90 % volume reduction, but due to unintentionally using a more concentrated feed in the study with GR70PP, the final protein concentration in the retentate was approximately doubled compared to the retentate of RC70PP. From this, the conclusion was drawn that GR70PP performed the best, as the flux was similar as when using RC70PP, but at a higher concentration of solutes. Diafiltration was performed with RC70PP with a diafiltration factor of 5 and this successfully decreased the conductivity in the retentate with approximately 50%, but it was at the cost of a small protein loss.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".