Journal
MATERIALS AND MANUFACTURING PROCESSES
Volume 24, Issue 3, Pages 320-330Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/10426910802679386
Keywords
Evolutionary algorithm; Genetic algorithms; Leaching; Manganese; Multiobjective optimization; Neural network; Ocean nodules; Optimization; Pareto frontier
Funding
- Akademi of Finland
- Ministry of Earth Sciences, India
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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.
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