Selectivity and competitive mechanism of cation exchange resin for Fe, Mg, and Al ions in phosphoric acid
Bibliographic record
Abstract
Abstract The presence of iron, magnesium, and aluminum elements as the primary impurities in wet‐process phosphoric acid (WPA) adversely affects the industrial phosphoric acid and subsequent phosphorus chemical products. This study aims to investigate the selectivity and competition mechanism of Sinco‐430 cation exchange resin for Fe, Mg, and Al ions in phosphoric acid solution. By studying the effects of different process conditions on the removal efficiency, the suitable conditions for the static removal of metal ions from Fe‐Mg, Al‐Mg, and Fe‐Al binary systems were determined: solid–liquid mass ratio (S/L) of 0.3, phosphoric acid concentration of 27.61 wt.%, system temperature of 50°C, and rotational speeds of 200, 400, and 200 rpm, respectively. By calculating the selectivity coefficients of the resin for metal ions under different experimental conditions and mutual replacement experiments, the semi‐empirical formulas for the selectivity coefficients were derived and order of selectivity was determined as follows: Mg2+ > Fe2+ > Al3+. Visual MINTEQ 3.1 software and density functional theory (DFT) calculations demonstrated that at low pH, the main forms of Fe, Mg, and Al present in phosphoric acid were FeH2PO4+, Mg2+, and AlH2PO42+, respectively. This finding explained the differences in selectivity of the resin for Fe, Mg, and Al. The dynamic removal of metal ions from phosphoric acid was investigated. The order of metal ion selectivity of the resin by the dynamic method is the same as that of the static method, and the dynamic exchange behaviour was most consistent with the Yan model.
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 | 0.000 |
| 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".