Optimal performance of an immersed membrane bioreactor equipped with a draft tube for domestic wastewater reclamation
Bibliographic record
Abstract
One of the options to prevent membrane fouling is to implement air lifting that can improve the cake removal from the membrane surface. This study presents the results of tests that were carried out at the Institutes for Desert Research, Kiryat Sde-Boker, Israel, and focused on the influence of hydrodynamic conditions on fouling in a pilot-scale immersed membrane bioreactor (IMBR) using a hollow fiber membrane module of ZW-10 (Zenon Environmental, Canada) under ambient conditions. In this system, the cross-flow velocities across the membrane surface were induced by one conical and four cylindrical draft-tubes. The relationship between the crossflow velocity and the aeration intensity, the influence of the crossflow on fouling rate under various hydrodynamic conditions were investigated and optimal operating conditions were obtained. Optimal operating conditions were reached during the long-term experiment period (70 days) for the treatment of domestic wastewater. The system was stable without external chemical cleaning. The results showed that the permeate was of high quality, and the removal of COD and BOD was 94.0% and 98.8%, respectively. The crossflow near the membrane surface reveals a major contribution for minimizing membrane fouling, and could offer guidelines for future design of similar systems.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".