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Lipase-catalyzed synthesis of dilauryl azelate ester: process optimization by artificial neural networks and reusability study

机译:脂肪酶催化的稀释亚脲酯的合成:通过人工神经网络和可重复使用性研究的过程优化

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摘要

An application of artificial neural networks (ANNs) to predict the performance of a lipase-catalyzed synthesis for esterification of dilauryl azelate ester was carried out. The central composite rotatable design (CCRD) experimental data were utilized for training and testing of the proposed ANN model. The model was applied to predict various performance parameters of the enzymatic reaction conditions, namely enzyme amount (0.05-0.45 g), reaction time (90-450 min), reaction temperature (40-64 degrees C) and molar ratio of substrates (AzA : LA, 1 : 3-1 : 9 mol). The incremental back propagation (IBP), batch back propagation (BBP), quick propagation (QP), genetic algorithm (GA), and the Levenberg-Marguardt (LM) algorithms were used in the network. It was found that the optimal algorithm and topology were the incremental back propagation (IBP) and the configuration with 4 inputs, 14 hidden, and 1 output nodes, respectively.
机译:进行了人工神经网络(ANNS)预测脂肪酶催化合成酯化酯化酯化酯化酯酯的合成的性能进行。 中央复合可旋转设计(CCRD)实验数据用于培训和测试所提出的ANN模型。 应用模型以预测酶反应条件的各种性能参数,即酶量(0.05-0.45g),反应时间(90-450min),反应温度(40-64℃)和基材的摩尔比(AZA :La,1:3-1:9 mol)。 在网络中使用了增量反向传播(IBP),批量回波传播(BBP),快速传播(QP),遗传算法(GA)和Levenberg-Marguardt(LM)算法。 有发现,最佳算法和拓扑是增量反向传播(IBP)和具有4个输入,14个隐藏和1个输出节点的配置。

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  • 来源
    《RSC Advances》 |2015年第115期|共10页
  • 作者单位

    Univ Putra Malaysia Fac Sci Dept Chem Serdang 43400 Selangor Malaysia;

    Univ Putra Malaysia Fac Sci Dept Chem Serdang 43400 Selangor Malaysia;

    Univ Putra Malaysia Fac Sci Dept Chem Serdang 43400 Selangor Malaysia;

    Univ Putra Malaysia Fac Sci Dept Chem Serdang 43400 Selangor Malaysia;

    Univ Putra Malaysia Fac Sci Dept Chem Serdang 43400 Selangor Malaysia;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 化学;
  • 关键词

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