Chemical Clogging and Evolution of Head Losses in Steel Slag Filters Used for Phosphorus Removal
Bibliographic record
Abstract
The objective of this study was to propose a conceptual model of clogging in alkaline granular filters. Two slag columns were operated for 600 days and monitored using piezometers and tracer tested at regular intervals. The type of influent (organic or inorganic) affected the loss of effective porosity in the filters. Well organized and loose crystal structures were observed by scanning electron microscopy in columns with inorganic and organic influents, respectively. It was postulated that the formation of crystals in unorganized structures results in confined voids that are not accessible for water flow, thus accelerating porosity loss. The effect of the combination of chemical clogging and biofilm on the porosity loss is higher than the effect of these two factors separately. The Kozeny-Carman equation for hydraulic conductivity could not efficiently predict the evolution of head losses in the column fed with an inorganic influent. The crystal structure and connectivity in the presence of homogeneous or heterogeneous precipitation are concepts that could improve predictions of hydraulic conductivity. The results of this study highlighted the importance of the inlet zone on the development of pressure head in alkaline granular filters. Future research on clogging should focus on precipitation mechanisms in the inlet zone and on the design of the feeding system.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".