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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 which usually results 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 and 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 using the Pareto method to arrive at a set of solutions. The appropriate configuration based on technical or economical 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%). The feed to the flotation circuit was characterized based on size fractions and their flotation rate constants. Results showed that with 95% confidence, the absolute difference between 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 65.7% while producing concentrate ash content (10.9%) within acceptable limits. By adding one stage to the current three-stage circuit, it was predicted that yield could further increase by 3.8% while keeping the quality of concentrate within an appropriate level.

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		KMPC Hemmat A Mineral Processing Digital Library
	All Years to Access Full-Text Export citations PARETO BASED OPTIMIZATION OF FLOTATION CELLS CONFIGURATION USING AN ORIENTED GENETIC ALGORITHM By D Pirouzan, M Yahyaei, S Banisi Published in International Mineral Processing Congress (IMPC) at 2012 Direct link: http://kmpchemmat.ir/pii/41574 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 which usually results 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 and 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 using the Pareto method to arrive at a set of solutions. The appropriate configuration based on technical or economical 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%). The feed to the flotation circuit was characterized based on size fractions and their flotation rate constants. Results showed that with 95% confidence, the absolute difference between 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 65.7% while producing concentrate ash content (10.9%) within acceptable limits. By adding one stage to the current three-stage circuit, it was predicted that yield could further increase by 3.8% while keeping the quality of concentrate within an appropriate level.