Solubility of Pb(II) and Ni(II) in Mixed Sulfate−Chloride Solutions with the Mixed Solvent Electrolyte Model
Bibliographic record
Abstract
The existence of Pb(II) in nickel sulfide mineral feedstocks imposes a burden to nickel hydrometallurgy. To devise effective Pb(II) control strategies, the solubility of Pb(II) in mixed sulfate−chloride solutions needs to be known in the nickel industry. In this work, the solubility of Pb(II) and Ni(II) in a Pb−Na−Ni−SO 4 −Cl−H 2 O system was modeled with the mixed solvent electrolyte (MSE) model through commercial software OLI systems. The model parameters, including equilibrium constants of solids (PbCl 2, PbSO 4, NiSO 4 · n H 2 O, and NiCl 2 · n H 2 O) and MSE ion interaction parameters, were regressed from various types of equilibrium and thermophysical data such as solubility, heat capacity, vapor pressure, and mean activity coefficient. The obtained model parameters are capable of accurately representing experimental data in binary (PbCl 2 −H 2 O, PbSO 4 −H 2 O, NiSO 4 −H 2 O, and NiCl 2 −H 2 O) and ternary (PbCl 2 −HCl/NaCl−H 2 O, NiSO 4 −Na 2 SO 4 −H 2 O, and NiCl 2 −HCl−H 2 O) systems and give excellent predictions in multicomponent systems (NiCl 2 −NaCl−H 2 O, PbSO 4 −H 2 SO 4 −H 2 O, and PbSO 4 −H 2 SO 4 −HCl−H 2 O).
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".