Reaction Engineering with Recurrent Neural Network: Kinetic Study of Dushman Reaction
Reaction Engineering with Recurrent Neural Network: Kinetic Study of Dushman Reaction
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DOI：
10.1016/j.ceja.2021.100219
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发表时间：
2021-11
影响因子：
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通讯作者：
Yuya Murakami;A. Shono
中科院分区：
文献类型：
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作者：
Yuya Murakami;A. Shono
首次将递归神经网络（RNN）用于预测Dushman反应的时间演化。通过引入四个重要特征优化了RNN的结构，以减少训练时间和所需的数据量：浓度变化作为输出;神经网络中的对数尺度计算;质量平衡计算;以及使用两个不同的速率定律对RNN小区进行预训练，然后进行微调。预训练的RNN模型显示出与使用原始速率定律积分的方法相似的准确性（每种速率律的均方误差分别为3.30和1.28 μ M2）。无论原始速率定律的准确性如何，微调后的模型都表现出显著的改进（两种速率定律的均方误差约为0.10 μ M2）。在微调过程中没有观察到过度训练，因为实施了对数尺度和质量平衡计算，这有助于RNN模型有效地从分散在几个数量级的训练数据中学习，并避免理论上无效的曲率。所提出的RNN模型适合于实际应用，因为它可以在有限的训练时间和有限的实验数据量（在这项工作中仅使用88个条件来准备训练数据集）的情况下实现高精度，并且不需要详细研究反应机理。结果表明，如果通过上述特征适当地引入理论背景，则RNN模型在反应动力学方面具有高度通用性。
A recurrent neural network (RNN) was used to predict the time evolution of the Dushman reaction for the first time. The structure of the RNN was optimized to reduce the training time and amount of data required by introducing four significant features: concentration change as an output; log-scale calculation in the neural network; mass balance calculation; and pre-training of an RNN cell using two different rate laws, followed by fine-tuning.The pre-trained RNN models showed similar accuracy to that of a method using integration of the original rate laws (mean squared error of 3.30 and 1.28 μM2for each rate law), as expected. The fine-tuned models exhibited significant improvements (mean squared error of approximately 0.10 μM2for both rate laws) regardless of the accuracy of the original rate laws. No overtraining was observed during the fine-tuning because of the implementation of log-scale and mass balance calculations, which helped the RNN models to effectively learn from training data scattered by several orders of magnitude and avoid theoretically invalid curvature. The proposed RNN model is suitable for practical applications because it can achieve high accuracy with limited training time and a limited amount of experimental data (only 88 conditions were used to prepare training dataset in this work) and does not require detailed investigation of the reaction mechanism. The results show that if a theoretical background is properly introduced by the features mentioned above, the RNN models are highly versatile in terms of reaction kinetics.