Combination of Slag, Limestone and Sedimentary Apatite in Columns for Phosphorus Removal from Sludge Fish Farm Effluents
Bibliographic record
Abstract
Laboratory scale studies have repeatedly reported high P-retention in slag, a by-product of the steel manufacturing industry. Thus, it has emerged as a potential material to increase P-removal from constructed wetlands (CWs). However, several limitations were highlighted by field experiments, including the high pH of treated water and clogging. We hypothesized that the addition of sedimentary rocks to slag would preserve P-removal properties while reducing the pH of treated water. Four 2.5 L-columns were filled with 100% apatite (column A); a 50% weight each mixture of limestone with apatite (column B); 10% steel slag located at the inlet, plus 45% limestone mixed with 45% apatite (column C); and a mixture of steel slag (10%), limestone (45%) apatite (45%) (column D). A synthetic effluent (26 mg P/L) and a reconstituted sludge fish farm effluent containing 97 mg/L total suspended solids (TSS), 220 mg/L chemical oxygen demand (COD) and 23.5 mg P/L phosphorus (P) were applied sequentially during 373 and 176 days, under saturated flow conditions and 12–24 hours hydraulic residence time (HRT), respectively. Treatment performance, P-removal, pH and calcium (Ca2+) were monitored. Results indicated that columns that contained 10% weight steel slag resulted in a higher P retention capacity than the columns without steel slag. The highest P removal was achieved in column C, containing a layer of slag in the inlet zone, 45% apatite and 45% limestone. Feeding the columns with a reconstituted fish farm effluent led to biofilm development, but this had little effect on the P-removal. A combination of slag and sedimentary rocks represents a promising filtration material that could be useful downstream of CWs to further increase P-removal.
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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".