Modelling of cross‐flow microfiltration coupled with bentonite treatment in sugar beet molasses purification
Bibliographic record
Abstract
Abstract The removal of colourants and other non‐sucrose compounds from sugar beet molasses opens the possibility for more acceptable and effective recovery of sucrose, which is the most valuable sugar industry product. In the presented study, the feasibility of the sugar beet molasses purification process, which combines sodium bentonite pre‐treatment and microfiltration with an embedded static mixer, was examined. Molasses pre‐treatment with sodium bentonite consisted of a bentonite suspension addition (7 g/L) under the following conditions: pH 5, temperature 50 °C, and contact time 30 min. Sugar beet molasses separation from bentonite was performed by cross‐flow microfiltration using a 200 nm TiO2 ceramic tubular membrane with static mixer absence or presence and variation in the sugar beet molasses flow rate and dry substance. The molasses purification process efficiency was validated through the permeate flux values as well as colour and turbidity measurements. Higher molasses colour (20–60 %) and turbidity reduction (83.4–99.2 %) was achieved in the experiments with static mixer absence, while static mixer introduction greatly increased the final and especially steady‐state permeate flux.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 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".