Recovery of nickel and cobalt as MHP from limonitic ore leaching solution: Kinetics analysis and precipitate characterization
Bibliographic record
Abstract
In the present study, precipitation of nickel and cobalt as mixed hydroxide precipitate (MHP) from pregnant leach solution of nickel limonite ore from Soroako after iron removal stage was carried out. A series of MHP precipitation experiments was conducted by using MgO slurry as neutralizing agent and the effects of pH, temperature, duration of precipitation and the addition of MHP seed on the precipitation behavior of nickel, cobalt, as well as iron and manganese was studied. Characterization of MHP product was performed by particle size analyzer (PSA) as well as X-Ray Fluorescence (XRF), X-Ray Diffractometer (XRD) and Scanning Electron Microscope (SEM) analyses. Kinetics analysis was made by using differential-integral method for the rate of homogenous reaction. Precipitation at pH 7, temperature 50°C for 30 minute, without seed addition resulted in nickel and cobalt recoveries of 82.8% and 92%, respectively with co-precipitated iron and manganese of 70% and 24.2%, respectively. The seed addition increases nickel and cobalt precipitations significantly to 99.9% and 99.1%, respectively. However, the addition of seed into led to a significant increase of manganese co-precipitation from 24.2% without seed addition to 39.5% at the addition of 1 g seed per 200 mL of PLS. Kinetics analysis revealed that Ni precipitation to form MHP follows the second-order reaction kinetics with activation energy of 94.6 kJ/mol.
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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".