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Multiobjective Methodology Applied in the Gasification of Coals Mixtures for the Analysis of the, Synthesis Gas Composition

机译:应用在煤混合物气化中的多目标方法进行分析,合成气体组合物

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An important aspect in the combustion of coal for purposes of generating clean power is to achieve that this solid fuel is gasified and hereby to capture the residues across different mechanisms and to use the gas of synthesis in the production of energy. Gasification has been widely studied but the amorphous characteristics of solid fuels causes the gasification reactions do not obey a defined order, however has been made possible prosecute kinetics of these reactions and orient products of synthesis gas, according to needs. In this regard, for purposes of power generation the hydrogen production at high rates is a problem of stability of the synthesis gas combustion, therefore their generation in the gasification should be controlled by the generation of methane priority and carbon monoxide. The objective of this work is to provide guidance with a theoretical tool to establish the optimal mix of solid fuels in relation to the gasifying agents to produce a synthesis gas with appropriate levels of hydrogen, for which genetic algorithms are used due to approach a problem nonlinear and multiple variables.The variables that control the generation of products of synthesis gas, corresponds to the amount of steam and oxygen /air relative to fuel flow fed to the gasification reactor. The results show that there may be many possibilities for feeding the gasifier, but there are defined relationships that can control with some limitations the hydrogen production in convenient relationships with carbon monoxide. In the third multiobjetive runEn la tercera corrida del algoritmo multiobjetivo, se tiene la menor cantidad de cenizas y una participation muy alta de los carbones del Cesar en la mezcla. In the third run of the multiobjective algorithm, it has the least amount of ash and a very high share of coal in the mix of Cesar.
机译:用于产生清洁功率的煤的燃烧中的一个重要方面是实现这种固体燃料的气化,从而捕获不同机制的残基并在能量的生产中使用合成气体。气化已被广泛研究,但固体燃料的无定形特性导致气化反应不服从定义的顺序,然而,根据需求,已成为这些反应和定向的合成气产品的检测动力学。在这方面,出于发电的目的,高速率的氢产生是合成气燃烧的稳定性的问题,因此它们在气化中的产生应通过产生甲烷优先级和一氧化碳来控制。这项工作的目的是提供具有理论工具的指导,以建立与气化剂相对于气化剂的最佳燃料混合,以产生具有适当水平的合成气,因为遗传算法由于接近问题非线性而使用遗传算法和多个变量。控制合成气产品的产生的变量对应于蒸汽和氧气/空气相对于进料到气化反应器的燃料流量的量。结果表明,喂食气化器可能存在许多可能性,但是有确定的关系可以控制氢气产生的一些限制与一氧化碳的方便关系。在第三个多元化漫游漫游La Tercera Corrida del Algoritmo Multibejetivo,Se Tiene La Menor Cantidad de Cenizas Y Una参与Muy Alta de Los Carbones Del Cesar en La Mezcla。在多目标算法的第三次运行中,它具有最少量的灰分和塞卡尔混合中的煤的份额。

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