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

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Pettersson F, Biswas A, Sen PK, Saxen H, Chakraborti N
Publisher: TAYLOR & FRANCIS INC
Publication year: 2009
Journal: Materials and Manufacturing Processes
Journal acronym: MATER MANUF PROCESS
Volume number: 24
Issue number: 3
Start page: 320
End page: 330
Number of pages: 11
ISSN: 1042-6914


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.


Keywords

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

Last updated on 2019-19-11 at 05:53