Pareto based optimization of flotation cells configuration using an oriented genetic algorithm

Pirouzan, D., Yahyaei, M. and Banisi, S. (2014) Pareto based optimization of flotation cells configuration using an oriented genetic algorithm. International Journal of Mineral Processing, 126 107-116. doi:10.1016/j.minpro.2013.12.001


Author Pirouzan, D.
Yahyaei, M.
Banisi, S.
Title Pareto based optimization of flotation cells configuration using an oriented genetic algorithm
Journal name International Journal of Mineral Processing   Check publisher's open access policy
ISSN 0301-7516
Publication date 2014-01-01
Year available 2013
Sub-type Article (original research)
DOI 10.1016/j.minpro.2013.12.001
Open Access Status Not Open Access
Volume 126
Start page 107
End page 116
Total pages 10
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Language eng
Abstract It is customary to use more than one stage of flotation to obtain an acceptable level of separation of valuables. Flotation circuit design is usually accomplished by applying empirical rules or by using expertise of practitioners which may result in operations not working at their optimum conditions. Given the capabilities of genetic algorithms (GA) in finding an optimum solution in very disturbed search spaces, they can be used to obtain the desired flotation circuit configuration. In flotation circuit configuration optimization problem, a combination of metallurgical parameters such as yield and concentrate ash content could be used as the fitness function for the genetic algorithm. The multi-objective nature of the problem justified the use of the Pareto method to arrive at a set of solutions. The appropriate configuration based on technical or economic considerations could then be chosen. To obtain the fitness function for any given configuration, it is necessary to model every flotation stage. The proposed method was used to find the optimum circuit configuration for a coal washing plant. The objective was to arrive at the highest yield while producing a concentrate with a certain ash content (11.2%). The feed to the flotation circuit was characterized based on the size fractions and their flotation rate constants. Results showed that with a 95% confidence the absolute difference between the modeled and measured values were 2.9-5.5% for yield and 0.4-1.1% for concentrate ash content. When the proposed GA-based circuit configuration was implemented in the plant, the yield increased from the original value of 57.6% to 64.3% while producing concentrate ash content (10.9%) within acceptable limits. By adding one stage to the current three-stage circuit, it was predicted that the yield could be further increased by 3.8% while keeping the quality of the concentrate within the appropriate level.
Keyword Flotation circuit configuration
Genetic algorithms
Modeling
Optimization
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ
Additional Notes Available online: 13 December 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Julius Kruttschnitt Mineral Research Centre Publications
Non HERDC
 
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Created: Tue, 11 Mar 2014, 20:25:50 EST by Karen Holtham on behalf of Julius Kruttschnitt Mineral Research Centre