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首页> 外文期刊>Materials and Manufacturing Processes >Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies
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Data-Driven Multiobjective Analysis of Manganese Leaching from Low Grade Sources Using Genetic Algorithms, Genetic Programming, and Other Allied Strategies

机译：数据驱动的低品位锰浸出的多目标分析，采用遗传算法，遗传规划和其他相关策略

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摘要

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.View full textDownload full textKeywordsEvolutionary algorithm, Genetic algorithms, Genetic programming, Leaching, Manganese, Multiobjective optimization, Ocean nodules, Optimization, Pareto frontierRelated var addthis_config = { ui_cobrand: "Taylor & Francis Online", services_compact: "citeulike,netvibes,twitter,technorati,delicious,linkedin,facebook,stumbleupon,digg,google,more", pubid: "ra-4dff56cd6bb1830b" }; Add to shortlist Link Permalink http://dx.doi.org/10.1080/10426914.2010.544809
机译：基于遗传算法，神经网络和遗传程序，采用各种自然启发策略构建了数据驱动的模型，用于各种低品位锰资源的浸出过程，并再次使用几种进化方法进行了双目标帕累托优化。为此目的使用了商用软件和内部代码，并且相互抵触。结果导致了在最大化回收率（这是一个以利润为导向的要求）与最小化酸消耗（这是解决环境问题）之间的最佳权衡。结果导致了一个非常复杂的场景，通常会通过不同的方法显示出不同的趋势，并对其进行了系统地分析。查看全文下载全文关键字进化算法，遗传算法，遗传编程，浸出，锰，多目标优化，海洋结节，优化，帕累托frontierRelated var addthis_config = {ui_cobrand：“泰勒和弗朗西斯在线”，servicescompact：“ citeulike，netvibes，twitter，technorati，delicious，linkedin，facebook，stumbleupon，digg，google，更多”，发布号：“ ra-4dff56cd6bb1830b”};添加到候选列表链接永久链接http://dx.doi.org/10.1080/10426914.2010.544809

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