We present a Keck/MOSFIRE rest-optical composite spectrum of 16 typical gravitationally lensed star-forming dwarf galaxies at 1.7 ≲
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/SiO2at 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
- Award ID(s):
- 2045550
- NSF-PAR ID:
- 10385832
- Publisher / Repository:
- IOP Publishing
- Date Published:
- Journal Name:
- Journal of Physics: Energy
- Volume:
- 5
- Issue:
- 1
- ISSN:
- 2515-7655
- Page Range / eLocation ID:
- Article No. 014009
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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