Investigation of flux dependence on viscosity in ultrafiltration
Bibliographic record
Abstract
This project aims to compare the effect of viscosity on flux for high viscosity liquids. The performance of two different viscous solutions was studied in a plate-and-frame membrane module. The solutions used were based on molasses, which is a liquid containing mainly low-molar mass components and konjac glucomannan (KGM) which is a substance with high molar mass. The change of the mass transport through the membrane (flux) during ultrafiltration of molasses and KGM as concentration (dry matter) was increased was investigated. The Alfa Laval RC70PP membrane and the Alfa Laval LabStak M39 equipment were used in the experiments. Additionally, this study also aims to model and simulate the concentration and viscosity gradient in proximity to the membrane and its effect on the flux by using COMSOL Multiphysics. Simulation of the concentration at the membrane surface is to be used to study different operational parameters effect on the flux at varying viscosities. In this case, a clear dependence of flux on viscosity was established. For the molasses, there was little retention and the flux was mainly influenced by permeate viscosity. The KGM experiments resulted in permeate viscosity values independent of feed viscosity, the flux was however greatly influenced by increasing hydraulic membrane resistance with higher feed viscosity. The viscosity effect on flux for molasses was successfully modelled in COMSOL. In the simulation, the concentration gradient in this case was found to have little effect on the flux.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".