Neural network estimation of kinetic parameters in distributed activation energy model (DAEM) without a priori assumptions for parallel reaction system
Neural network estimation of kinetic parameters in distributed activation energy model (DAEM) without a priori assumptions for parallel reaction system
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DOI：
10.1016/j.fuel.2023.127836
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发表时间：
2023
期刊：
影响因子：
7.4
通讯作者：
Shinji Wakimoto;Yoshiya Matsukawa;Yui Numazawa;Y. Matsushita;H. Aoki
中科院分区：
文献类型：
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作者：
Shinji Wakimoto;Yoshiya Matsukawa;Yui Numazawa;Y. Matsushita;H. Aoki
在这项研究中，一个新的估计方法的动力学参数的分布活化能模型（DAEM）的设计和发展。该方法将DAEM法的转化率估计过程看作是一个三层神经网络的前馈计算过程，通过神经网络的优化来估计DAEM法的动力学参数。该方法不需要对平行反应体系的动力学参数或反应机理进行预先假设。首先，我们使用数值模拟创建反应数据，并使用神经网络进行动力学分析。神经网络预测转化率非常准确，然而，也出现了对平行反应系统贡献较低的反应。其次，我们利用神经网络对第i个反应贡献的下限Vi */V* 进行了动力学分析。Vi */V* 的下限不影响X的预测精度，但对减少低Vi */V* 的反应和DAEM中动力学参数的估计精度有显著影响。当采用隐层节点数为64的神经网络时，Vi */V* 的最佳下限为1.0×10−5 ~ 1.0×10− 4。此外，该方法的预测精度与传统的方法-单高斯方法（1-DAEM），双高斯方法（2-DAEM），和Miura和Maki方法进行了比较。与传统方法相比，该方法估算的动力学参数更接近真实值。此外，该方法预测的X值比传统方法预测的X值更准确。还研究了近似对训练数据创建的影响。神经网络的估计精度仍然很高，但稍微恶化时，神经网络的优化使用的反应数据创建没有近似。
In this study, a new estimation method for the kinetic parameters in a distributed activation energy model (DAEM) was designed and developed. In the proposed method, the conversion estimation by the DAEM is regarded as a feedforward computation of a three-layer neural network, and the kinetic parameters of the DAEM are estimated by optimization of the neural network. The proposed method does not require ana prioriassumption of the kinetic parameters or mechanism of a parallel reaction system. First, we created reaction data using numerical simulations, and a kinetic analysis using the neural network was performed. The neural network predicted conversionXvery accurately; however, reactions with low contributions to the parallel reaction system also appeared. Next, we carried out a kinetic analysis using the neural network with the lower limit on the contribution of theith reactionVi*/V*. The lower limit onVi*/V* did not influence the prediction accuracy ofXand had a significant effect on reducing the reactions with lowVi*/V* and the estimation accuracies of the kinetic parameters in the DAEM. The optimal value of the lower limit onVi*/V* was determined to be 1.0×10−5–1.0×10−4when using the neural network with 64 hidden layer nodes. Moreover, the prediction accuracy of the proposed method was compared with those of conventional methods — the single-Gaussian method (1-DAEM), double-Gaussian method (2-DAEM), and Miura and Maki method. The kinetic parameters estimated using the proposed method were closer to the true values than those obtained using conventional methods. Moreover, theXvalue predicted by the proposed method was more accurate than that predicted by conventional methods. The influence of approximation on training data creation was also examined. The estimation accuracy of the neural network was still high but slightly deteriorated when the neural network was optimized using the reaction data created without the approximation.