Energy Consumption Related to Shear Stress For Membrane Bioreactors Used For Wastewater Treatment
Bibliographic record
Abstract
The main drawback of membrane bioreactors (MBR) systems is the fouling of the membrane, which is decrease and/or prevented through gas sparging. However, gas sparging practices are based on rules of thumb or a trial-and-error approaches which are tedious, very timeconsuming, do not necessarily provide optimal fouling control and they are not energy efficient. Therefore, dedicated experiments are needed to fully understand the hydrodynamics of this two-phase flow. Two types of submerged MBRs were studied: 1) Hollow Fiber (HF) and 2) Hollow Sheet (HS). For the first case, experimental shear stress measurements and CFD simulation were made. It was found that the average shear stress over the membrane surface from the CFD model is similar compared to experimental data (error less than 8 %). However, some differences in the distribution of shear stress throughout the submerged MBR system were observed. It was found that the CFD and experimental data was similar in terms of shear stress. On the other hand, for the HS MBR experimental measurements were not made. Nevertheless, as a proper validation was attained with the HF MBR, it was inferred that the CFD results for the HS MBR were accurate. A linear empirical correlation between the average shear stress and the blower power per unit of permeate was made. This work uses an empirical relationship to determine the shear stress based on the ratio of aeration blower power to tank volume. This relationship is used in bubble column reactors and it is extrapolate to determine shear stress on MBR systems. It was found that this relationship is over predictive by 28 % compared to experimental measurements and CFD results. Therefore a correction factor is included on the relationship to account for the membrane placed inside the bioreactor.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".