Analysis of shear stress and energy consumption in a tubular airlift membrane system
Bibliographic record
Abstract
Application of a two-phase slug flow in side-stream membrane bioreactors (MBRs) has proven to increase the permeate flux and decrease fouling through a better control of the cake layer. Past literature has shown that the hydrodynamics near the membrane surface have an impact on the degree of fouling by imposing high shear stress near the surface of the membrane. Previously, shear stress histograms (SSH) have been introduced to summarize results from an experimental setup developed to investigate the shear stress imposed on the surface of a membrane under different two-phase flow conditions (gas and liquid) by varying the flow of each phase. Bimodal SSHs were observed, with peaks corresponding to the shear induced by the liquid and gas flow respectively. In this contribution, SSHs are modelled using simple empirical relationships. These are used to identify the two-phase flow conditions that optimize fouling control. Furthermore, the total energy consumption of the system was estimated based on the two-phase pressure drop. It was found that low liquid and high gas flow rates (ratio of approx. 4) balanced the peaks and minimized the energy consumption.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".