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Optimization study on recovery hydrocarbons from pyrolysis oil using ionic liquid

机译:离子液体从热解油中回收烃的优化研究

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摘要

Energy crisis is a global issue because of fossil oil shortage. Biomass energy is a potential succedaneum for solving this problem, and fast pyrolysis is a effective route to convert biomass to liquid product. However the quality of pyrolysis oils is still a serious bottleneck for it service as transport fuel. Thus many study focused on how to improve it with physical or chemical methods. In this study, ionic liquid (1-butyl-3-methylimidazolium chloride, [BMIm]Cl) was used to separate hydrocarbon and improve the content in simulated pyrolysis oils which consisted some typical components. The optimization procedure was based on the Back Propagation (BP) Artificial Neural Network (ANN) modeling with Genetic Algorithm (GA). It was found that the hydrocarbon yield and content was increasing when oils to [BMIm]Cl ratio decreased, and the extraction system can be reached to equillibrium in a short time. Optimal condition was set as which extraction and holding time were at 20 and 6 minutes and oils to [BMIm]Cl ratio at 1.2 : 1, and hydrocarbon yield and content were 61.41% and 80.21% respectively as result. Finally, [BMIm]Cl is recycled by water extraction from waste. Comparing with original [BMIm]Cl, the result shows that hydrocarbons upgrading performance using recycled [BMIm]Cl is still acceptable.
机译:由于化石燃料短缺,能源危机是一个全球性问题。生物质能是解决这一问题的潜在成功方法,而快速热解是将生物质转化为液体产品的有效途径。然而,热解油的质量仍然不能用作运输燃料。因此,许多研究集中在如何通过物理或化学方法进行改进。在这项研究中,使用离子液体(1-丁基-3-甲基咪唑鎓氯化物,[BMIm] Cl)分离烃并提高模拟热解油中的含量,该热解油由一些典型成分组成。优化程序基于带有遗传算法(GA)的反向传播(BP)人工神经网络(ANN)建模。发现当油与[BMIm] Cl的比例降低时,烃的收率和含量增加,并且可以在短时间内使萃取系统达到平衡。设定最佳条件为:萃取和保持时间分别为20分钟和6分钟,油与[BMIm] Cl的比例为1.2:1,结果烃产率和含量分别为61.41%和80.21%。最后,[BMIm] Cl通过从废物中提取水而再循环。与原始的[BMIm] Cl相比,结果表明,使用循环的[BMIm] Cl进行烃类提质的性能仍然可以接受。

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  • 作者

    Wang Jun Jie;

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  • 年度 2012
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  • 原文格式 PDF
  • 正文语种 en
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