Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies.

Autor:	Biswas, Arijit, Maitre, Ogier, Mondal, DebangaNandan, Das, SyamalKanti, Sen, ProdipKumar, Collet, Pierre, Chakraborti, Nirupam
Předmět:	MANGANESE GENETIC algorithms GENETIC programming NEURAL computers ARTIFICIAL neural networks PARETO analysis
Zdroj:	Materials & Manufacturing Processes; Mar2011, Vol. 26 Issue 3, p415-430, 16p, 5 Charts, 25 Graphs
Abstrakt:	Data-driven models are constructed for leaching processes of various low grade manganese resources using various nature inspired strategies based upon genetic algorithms, neural networks, and genetic programming and subjected to a bi-objective Pareto optimization, once again using several evolutionary approaches. Both commercially available software and in-house codes were used for this purpose and were pitted against each other. The results led to an optimum trade-off between maximizing the recovery, which is a profit oriented requirement, along with a minimization of the acid consumption, which addresses the environmental concerns. The results led to a very complex scenario, often with different trends shown by the different methods, which were systematically analyzed. [ABSTRACT FROM AUTHOR]
Databáze:	Complementary Index
Externí odkaz:	Zobrazit plný text záznamu Plný text ve formátu HTML
Nepřihlášeným uživatelům se plný text nezobrazuje	K zobrazení výsledku je třeba se přihlásit.