• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Biotechnology Progress >Application of Iterative Robust Model-Based Optimal Experimental Design for the Calibration of Biocatalytic Models
【24h】

Application of Iterative Robust Model-Based Optimal Experimental Design for the Calibration of Biocatalytic Models

机译：基于迭代鲁棒模型的最优实验设计在生物催化模型校准中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The aim of model calibration is to estimate unique parameter values from available experimental data, here applied to a biocatalytic process. The traditional approach of first gathering data followed by performing a model calibration is inefficient, since the information gathered during experimentation is not actively used to optimize the experimental design. By applying an iterative robust model-based optimal experimental design, the limited amount of data collected is used to design additional informative experiments. The algorithm is used here to calibrate the initial reaction rate of an x-transaminase catalyzed reaction in a more accurate way. The parameter confidence region estimated from the Fisher Information Matrix is compared with the likelihood confidence region, which is not only more accurate but also a computationally more expensive method. As a result, an important deviation between both approaches is found, confirming that linearization methods should be applied with care for nonlinear models. (C) 2017 American Institute of Chemical Engineers
机译：模型校准的目的是从这里估计可用的实验数据的独特参数值，其应用于生物催化过程。第一采集数据的传统方法，然后执行模型校准是低效的，因为在实验期间收集的信息不受积极地用于优化实验设计。通过应用基于迭代的鲁棒模型的最优实验设计，收集的有限数据用于设计额外的信息实验。这里使用该算法以更精确的方式校准X-转氨酶催化反应的初始反应速率。从Fisher信息矩阵估计的参数置信区与似然置信区的比较，这不仅更准确，而且还具有计算更昂贵的方法。结果，发现了两种方法之间的重要偏差，确认应用关心非线性模型应用线性化方法。 （c）2017美国化学工程师研究所

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号