Application of Membrane Technology to Slaughterhouse Blood to Produce Edible Powdered Protein Mixture
Bibliographic record
Abstract
Animal blood generated from slaughtered animals is often released into the environment resulting in significant pollution and also in the loss of a valuable protein source. This study aimed at developing a procedure that will allow for utilizing animal blood for protein powders production on an industrial scale. To meet this goal, hygienically collected animal blood was first treated with membrane technology: microfiltration (MF) or ultrafiltration (UF). A ceramic MF membrane and a PCI UF membrane were used. Average MF flux was 6.62 kg h-1 m-2 at transmembrane pressure of 2.5 bar. Average UF flux was 3.55 kg h-1 m-2 at transmembrane pressure of 4 bar. MF succeeded in separating the blood plasma proteins (permeate) from the red cell fraction (retentate). UF concentrated both the red cell and blood plasma proteins in a single sample (retentate). The volume ratio of retentate to permeate was 10:14 and 14:10, for the MF and UF, respectively. A membrane cleaning regime was developed. The treated blood fractions were then freeze dried and red and white protein powders were produced successfully. The potential of a SME (small-medium enterprise) to apply this procedure into practice is presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".