Ripening of granular media filters for pretreatment of seawater in membrane desalination
Bibliographic record
Abstract
The successful operation of a seawater reverse osmosis desalination plant depends greatly on the performance of the pretreatment step. The start-up of a pretreatment filtration process usually begins with a dynamic part called ripening, during which the filter media increases its adsorption ability and improves the removal of foulants from the seawater. In that period (typically lasting 30–60 min, depending on the system's conditions) the effluent often does not meet the quality requirements of the RO membranes and must often be disposed of. An observation and analysis of the ripening phenomena was performed using lab scale acrylic filter columns containing granular activated carbon. The seawater originated from the Yellow Sea in Korea and an inline coagulation was performed prior to filtration using an optimal dose of FeCl3 based on a jar test. The turbidity, total suspended solids concentration and dissolved organic carbon were measured before and after filtration with the goal of assessing the filtration performance. The filter media, which was cleaned and dried before filtration showed distinct ripening characteristics. The measurements were then used for parameter identification of a typical filtration model. Based on the fitted model one can predict the optimal filter depth that would reduce the waste stream production of the plant.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".