Mathematical and statistical modeling of glucose permeation through ultrafiltration system
Bibliographic record
Abstract
Membrane bioreactors (MBRs) have been recently proposed for enhancing enzymatic hydrolysis of lignocelluloses by simultaneously and selectively removing the produced sugars from the reaction system. An inverted dead-end MBR with polyethersulfone membrane was used to investigate glucose permeation. The effects of glucose concentration, water flowrate, and membrane molecular weight cut-offs (MWCO), were investigated. The developed diffusion-convective model predicted glucose permeation with R 2 value of 0.96. The statistical analysis showed that the effects of glucose concentration and water flowrate were significant, with P-value less than 0.05 for both, whereas that of the MWCO was insignificant (P-value of 0.66). This is the first attempt to mathematically describe the behavior of glucose across ultrafiltration membrane, while taking into consideration both molecular diffusion and convective flow. The findings of this work are essential for understanding the behavior and enhancing the performance of solute permeation through ultrafiltration membrane, which is the heart of many processes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".