Evaluation of fouling and RO performance for MBR treated fruit wastewater
Bibliographic record
Abstract
Fruit processing facilities are looking for ways to reduce water consumption to counter the impact of climate change. A good alternative is an MBR system to treat the processing wastewater, followed by tertiary treatment using a reverse osmosis (RO) unit to enable water reuse. However, fouling of the RO membrane causes operational challenges. As a result, experiments were completed on treated fruit processing wastewater to identify the causes of fouling that originated from the MBR effluent and develop best management practices (BMPs) to minimize fouling of the RO membrane. Physical and chemical analyses along with visual inspection of the membrane surface using scanning electron microscopy (SEM), energy diffusive X-ray (EDX) and Fourier transform infrared (FTIR) spectroscopy were completed. The issue of RO membrane fouling and subsequent flux decline was directly related to the presence of soluble microbial products, specifically dissolved organic matter (DOM) in the MBR effluent. The developed BMPs show that the previously completed enhanced coagulation-GAC sorption process, when combined with an online non-chemical flushing regimen and proper membrane preservation, keeps the flux readings high, resolving frequent fouling and cleaning problems of the RO membrane.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".