Rare Earth Separation Intelligence
Predict recovery, selectivity, and purity across separation trains.
Process overview
Rare earth separation converts mixed concentrate into individually separated, high-purity rare earth products through solvent extraction, precipitation, and calcination. Small drifts in reagent condition, pH, or feed assay propagate into recovery loss and impurity excursions confirmed only by delayed lab analysis.
Why prediction matters
Recovery and purity are the economic core of a separation plant, yet they are confirmed hours after value has already left the circuit. Prediction connects live stage conditions to the eventual product state so operators can intervene while the batch is still recoverable.
Key variables
Feedstock variables
- Feed assay by element
- Concentrate variability
- Impurity load
- Feed flow
Process variables
- Stage pH profile
- Organic-to-aqueous ratio
- Reagent / extractant condition
- Temperature
- Residence time
Output variables
- Element recovery
- Product purity
- Impurity carryover
- Selectivity
Common transformation risks
AI opportunities
- Recovery prediction
- Purity classification
- Impurity risk scoring
- Stage-loss attribution
Community discussions
- k.Question
k.almeida
Process data scientist · 5d
Which of these variables tends to carry the most predictive signal in practice? Curious where to focus feature work first.
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