Model of Phosphorus Precipitation and Crystal Formation in Electric Arc Furnace Steel Slag Filters
Bibliographic record
Abstract
The objective of this study was to develop a phosphorus retention mechanisms model based on precipitation and crystallization in electric arc furnace slag filters. Three slag columns were fed during 30 to 630 days with a reconstituted mining effluent at different void hydraulic retention times. Precipitates formed in columns were characterized by X-ray diffraction and transmission electronic microscopy. The proposed model is expressed in the following steps: (1) the rate limiting dissolution of slag is represented by the dissolution of CaO, (2) a high pH in the slag filter results in phosphorus precipitation and crystal growth, (3) crystal retention takes place by filtration, settling and growth densification, (4) the decrease in available reaction volume is caused by crystal and other particulate matter accumulation (and decrease in available reaction time), and (5) the pH decreases in the filter over time if the reaction time is too low (which results in a reduced removal efficiency). Crystal organization in a slag filter determines its phosphorus retention capacity. Supersaturation and water velocity affect crystal organization. A compact crystal organization enhances the phosphorus retention capacity of the filter. A new approach to define filter performance is proposed: saturation retention capacity is expressed in units of mg P/mL voids.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".