Analyzing Leaching Data for Low-Grade Manganese Ore Using Neural Nets and Multiobjective Genetic Algorithms

Frank Pettersson, A Biswas, PK Sen, Henrik Saxén, N Chakraborti

Research output: Contribution to journalArticleScientificpeer-review

70 Citations (Scopus)


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 languageUndefined/Unknown
Pages (from-to)320–330
Number of pages11
JournalMaterials and Manufacturing Processes
Issue number3
Publication statusPublished - 2009
MoE publication typeA1 Journal article-refereed


  • Evolutionary algorithm
  • Genetic algorithms
  • Leaching
  • Manganese
  • Multiobjective optimization
  • Neural network
  • Ocean nodules
  • Optimization
  • Pareto frontier

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