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

Tutkimustuotos: LehtiartikkeliArtikkeliTieteellinenvertaisarvioitu

60 Sitaatiot (Scopus)

Abstrakti

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.
AlkuperäiskieliEi tiedossa
Sivut320–330
Sivumäärä11
JulkaisuMaterials and Manufacturing Processes
Vuosikerta24
Numero3
DOI - pysyväislinkit
TilaJulkaistu - 2009
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

Keywords

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

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