A deep neural network for oxidative coupling of methane trained on high-throughput experimental data
A deep neural network for oxidative coupling of methane trained on high-throughput experimental data
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DOI：
10.1088/2515-7655/aca797
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Klea Ziu;Rubén Solozabal;S. Rangarajan;Martin Takác
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文献类型：
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作者：
Klea Ziu;Rubén Solozabal;S. Rangarajan;Martin Takác
在这项工作中，我们根据已发表的高通量实验催化数据开发了一个深度神经网络模型，用于甲烷氧化偶联反应速率。一个神经网络制定，使速率模型满足活塞流反应器的设计方程。然后使用该模型来了解参考催化剂Mn-Na 2 WO 4/SiO2在不同温度下反应器内反应物和产物组成的变化，并识别相对于参考催化剂可以提高产率和选择性的新催化剂和已知催化剂的组合。该模型揭示了甲烷在催化剂床的前半部分中转化，而第二部分在很大程度上固结产物（即增加乙烯与乙烷的比率）。对先前研究的M1（M2）1 - 2 M3 O x /载体形式的催化剂对（其中M1、M2和M3是金属）的1003400种组合的筛选研究表明，包括两个连续催化剂床的反应器配置导致协同效应，导致在相同条件和接触时间下与参比催化剂相比C2的产率增加。最后，对7400种组合（包括先前研究的金属，但具有几种新的排列）的扩展筛选研究揭示了具有提高的C2产物产率的多种催化剂选择。这项研究证明了直接从高通量数据中学习瞬时反应速率的深度神经网络模型的价值，并代表了约束数据驱动反应模型以满足域信息的第一步。
In this work, we develop a deep neural network model for the reaction rate of oxidative coupling of methane from published high-throughput experimental catalysis data. A neural network is formulated so that the rate model satisfies the plug flow reactor design equation. The model is then employed to understand the variation of reactant and product composition within the reactor for the reference catalyst Mn–Na2WO4/SiO2 at different temperatures and to identify new catalysts and combinations of known catalysts that would increase yield and selectivity relative to the reference catalyst. The model revealed that methane is converted in the first half of the catalyst bed, while the second part largely consolidates the products (i.e. increases ethylene to ethane ratio). A screening study of ⩾3400 combinations of pairs of previously studied catalysts of the form M1(M2) 1−2 M3O x /support (where M1, M2 and M3 are metals) revealed that a reactor configuration comprising two sequential catalyst beds leads to synergistic effects resulting in increased yield of C2 compared to the reference catalyst at identical conditions and contact time. Finally, an expanded screening study of 7400 combinations (comprising previously studied metals but with several new permutations) revealed multiple catalyst choices with enhanced yields of C2 products. This study demonstrates the value of learning a deep neural network model for the instantaneous reaction rate directly from high-throughput data and represents a first step in constraining a data-driven reaction model to satisfy domain information.