The efficiency of various chemical solutions to clean reverse osmosis membranes processing swine wastewater
Bibliographic record
Abstract
The increasing use of membrane technology to treat highly charged wastewaters has renewed interest in the development of adequate cleaning strategies. This study investigated the efficiency of various chemicals, including acids, bases, surfactants, chelators, salts, enzymes, and oxidants, to clean two reverse osmosis membranes (BW30 and SW30XLE) filtering one swine wastewater pretreated by aerobic biofiltration and two swine wastewaters pretreated by mechanical solid–liquid separation. Mixes of anionic surfactants and chelators provided optimal cleaning efficiency for all fouled membranes and all effluents. A solution containing 10 mM EDTA (ethylenediamine tetraacetic acid) and 10 mM SDS (sodium dodecyl sulfate) yielded the highest flux recovery after one 20-h fouling cycle with the BW30 membrane and three consecutive fouling–cleaning cycles with the SW30XLE membrane. The EDTA + SDS solution also resulted in the lowest residual protein concentration on membrane surface and the optimal restoration of the initial contact angle of the membranes. Conversely, 75 mM acid citric and 100 mM NaCl solutions were the least efficient to clean the fouled membranes. Most chemical solutions were more efficient to clean the fouling layer generated by the swine wastewater pretreated by aerobic biofiltration than mechanical separation.
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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) | 0.000 | 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) | 0.000 | 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".