An automated computational approach to kinetic model discrimination and parameter estimation,Reaction Chemistry & Engineering

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An automated computational approach to kinetic model discrimination and parameter estimation
Reaction Chemistry & Engineering ( IF 3.4 ) Pub Date : 2021-5-7 , DOI: 10.1039/d1re00098e
Connor J Taylor ₁ , Hikaru Seki ₂ , Friederike M Dannheim ₂ , Mark J Willis ₃ , Graeme Clemens ₄ , Brian A Taylor ₄ , Thomas W Chamberlain ₁ , Richard A Bourne ₁

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We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

中文翻译：

动力学模型判别和参数估计的自动计算方法

我们在此报告了一种新颖的、自动化的计算方法在化学反应网络 (CRN) 识别中的实验应用。该报告显示了在模型速率定律中考虑催化物种以及各种整数和非整数阶次时，自主工具的首次化学应用，用于识别过程的动力学模型和参数。这种动力学分析方法只需要输入化学系统中的物质（起始材料、中间体、产品等）。) 和相应的时间序列浓度数据，以确定目标化学的动力学信息。这是在最少的人为交互的情况下执行的，并且执行了几个案例研究以显示该过程开发工具的广泛范围和适用性。此处描述的方法可以使用来自任何来源的实验数据来采用，并且该方法的代码也是开源提供的。

更新日期：2021-05-14

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