Abstract
Existing acid leaching data for low-grade manganese ores are modeled using an evolving neural net. Three distinct cases of leaching in the presence of glucose, sucrose and lactose have been considered and the results compared with an existing analytical model. The neural models are then subjected to bi-objective optimization, using a predator-prey genetic algorithm, maximizing recovery in tandem with a minimization of the acid concentration. The resulting Pareto frontiers are analyzed and discussed.
| Original language | Undefined/Unknown |
|---|---|
| Pages (from-to) | 320–330 |
| Number of pages | 11 |
| Journal | Materials and Manufacturing Processes |
| Volume | 24 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - 2009 |
| MoE publication type | A1 Journal article-refereed |
Keywords
- Evolutionary algorithm
- Genetic algorithms
- Leaching
- Manganese
- Multiobjective optimization
- Neural network
- Ocean nodules
- Optimization
- Pareto frontier