Nettoyage éco-efficace de membranes planes et spirales d’ultrafiltration de lait écrémé : approches physico-chimiques et hydrodynamiques concertées
Bibliographic record
Abstract
Cleaning is a key step of membrane processes used in the food industry. It allows to restore the membrane flux and selectivity by eliminating irreversible fouling. Currently, cleaning hasn't been mastered, expensive and polluting, due to a lack of knowledge of the fundamental mechanisms of cleaning. This study follows an EcoDesign approach that aims to install membrane processes as sustainable production processes for the skim milk ultrafiltration with spiral wound module. 3 axis were followed: critical analysis with an EcoDesign approach of empiric cleaning processes in the industry, a study of dynamic protein adsorption/desorption mechanisms to understand the respective part of physicochemistry and hydrodynamic, a search for ecologically and economically efficient detergents. The efficiency of cleaning was measured with a traditional method, namely recovery of the membrane permeability, and with an original FTIR-ATR method, quantifying the concentration of residual proteins on the membrane surface. The latter turned out to be the best tool in order to evaluate the efficiency of membrane cleaning, whereas the flux can be misleading.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".