A deep neural network for oxidative coupling of methane trained on high-throughput experimental data. (1st January 2023)

• Record Type: Journal Article
• Authors: Ziu, Klea; Solozabal, Ruben; Rangarajan, Srinivas; Takáč, Martin
• Is Part Of: JPhys energy. Volume 5:Number 1(2023)
• Publication Date: 2023-01-01
• Subjects: deep neural networks -- oxidative coupling of methane -- high-throughput experiments -- kinetic model -- data-driven catalyst design -- residual neural network; Power resources -- Research -- Periodicals; Power resources -- Periodicals; 333.79
• DOI: 10.1088/2515-7655/aca797
• Languages: English
• ISSNs: 2515-7655

Abstract

Abstract: 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–Na2 WO4 /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.