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首页> 外文期刊>Journal of industrial and engineering chemistry >Kinetic modeling of oxidative dehydrogenation of propane (ODHP) over a vanadium-graphene catalyst: Application of the DOE and ANN methodologies
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Kinetic modeling of oxidative dehydrogenation of propane (ODHP) over a vanadium-graphene catalyst: Application of the DOE and ANN methodologies

机译：钒-石墨烯催化剂上丙烷氧化脱氢动力学模型（ODHP）：DOE和ANN方法的应用

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摘要

In this research the application of design of experiment (DOE) coupled with the artificial neural networks (ANN) in kinetic study of oxidative dehydrogenation of propane (ODHP) over a vanadium-graphene catalyst at 400-500 °C and a method of data collection/fitting for the experiments were presented. The proposed reaction network composed of consecutive and simultaneous reactions with kinetics expressed by simple power law equations involving a total of 20 unknown parameters (10 reaction orders and 5 rate constants each expressed in terms of a pre-exponential factors and activation energies) determined through non-linear regression analysis. Because of the complex nature of the system, neural networks were employed as an efficient and accurate tool to model the behavior of the system. Response surface methodology (RSM) and ANN methods were constructed based upon the DOE'S points and were then utilized for generating extra-simulated data. The three data sets including the original experimental data, those simulated by the ANN and RSM methods were subsequently used to fit power law kinetic rate expressions for the main ODHP and side reactions. The results of kinetic modeling with simulated data sets from the ANN and RSM models compared with collected experimental data. Both methods were able to satisfactorily fit the experimental data for which the ANN data set showed the best fitting amongst them all.
机译：在这项研究中，实验设计（DOE）结合人工神经网络（ANN）在钒-石墨烯催化剂上于400-500°C的丙烷氧化脱氢（ODHP）动力学研究和数据收集方法中的应用/适合实验。拟议的反应网络由连续和同时发生的反应组成，其动力学由简单幂定律方程式表示，该方程式涉及通过非反应性确定的总共20个未知参数（10个反应阶数和5个速率常数，分别以前指数因子和活化能表示）。 -线性回归分析。由于系统的复杂性，神经网络被用作建模系统行为的有效且准确的工具。基于DOE'S点构建了响应面方法（RSM）和ANN方法，然后将其用于生成额外的模拟数据。随后使用包括原始实验数据在内的三个数据集（通过ANN和RSM方法模拟的数据集）拟合主要ODHP和副反应的幂律动力学速率表达式。使用来自ANN和RSM模型的模拟数据集进行动力学建模的结果与收集的实验数据进行了比较。两种方法都能够令人满意地拟合实验数据，其中ANN数据集显示了所有方法之间的最佳拟合。

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