A Laboratory Study of the Treatability of Synthetic Stormwater Under Varying Conditions Using Electric Arc Furnace Steel Slag
Bibliographic record
Abstract
The investigation of electric arc furnace (EAF) steel slag as a viable add-on technology to existing stormwater systems for the removal of dissolved phosphorus (P) was extended to explore the effects of varying environmental and treatment system conditions. Parameters such as stormwater composition, P concentration, metal concentration, pH, temperature, slag mass and slag particle size were varied. Observations relating to the method of P removal via EAF slag were also carefully considered to explain removal mechanisms involved. Results demonstrated that, although physisorption contributed to P reduction, it was not the key P removal mechanism. Instead, precipitation was observed to be a key removal pathway as evidenced by the correlation between the loss of iron (Fe) from slag and the amount of P removed from solution. The reduced removal of P by slag in a copper-dominant stormwater solution was attributed to the formation of a stable complex formed by the interaction of copper with the slag via the ion-exchange surface model. The stability of this complex inhibits the loss of Fe from the EAF slag and, consequently, P removal by means of precipitation. In terms of the effect of changing environmental and treatment system conditions on the P removal process, stormwater composition, P concentration, metal concentration, pH, temperature, slag mass and slag particle size were found to significantly influence the effectiveness of EAF slag in removing P from a given stormwater system. It was also established that a number of combinations of these factors influence P uptake differently.
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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) | 0.001 | 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 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".