Modification of steel slag and its application for phosphate and ammonia removal in aquatic products processing wastewater
Bibliographic record
Abstract
This study aims to modify different types of steel slag by heating, characterise the material and test its ability for adsorption of phosphate and ammonia from aquatic products processing wastewater. The materials were characterised by SEM, EDS, BET and FT-IR analyses. The effects of the type of steel slag, calcination temperature in steel slag modification, pH, contact time, adsorbent mass and initial pollutant concentration were investigated using the one-factor-at-a-time approach. The results show that the adsorption kinetic of the samples followed the pseudo-second-order model, whereas their adsorption isotherm fitted well with the monomolecular adsorptive Langmuir with a maximum phosphate adsorption capacity of 45.045 mgPO43-/g. Optimisation using Response Surface Methodology was conducted with the independent factors (adsorbent doses, contact time and calcination temperature of steel slag) and response (removal efficiency of phosphate). The temperature and dosage of the material significantly affected phosphate removal efficiency and optimisation conditions were suggested. Validation of one optimum condition at the calcination temperature of 801°C, the adsorbent dosage of 11.9 g/L and the contact time of 72 min using real aquatic products processing wastewater resulted in the removal efficiencies of 86.93, 87.59, 7.76 and 7.58% for phosphate, total phosphorus, ammonia and total nitrogen, respectively.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".