Machine Learning-Aided Catalyst Modification in Oxidative Coupling of Methane via Manganese Promoter

To enhance CH4 conversion
values in oxidative coupling
of methane (OCM) reactions under an O2-lean condition (CH4/O2 = 6.0), a support vector regression (SVR) and
one-hot encoding manner implemented machine learning (ML) is examined.
From an open-source high-throughput screening (HTS) database of 300
OCM catalysts made by random sampling from a materials space, the
top 10 three-element-supported catalysts with C2 yield
higher than 11.0% and C2 selectivity higher than 80.0%
at CH4/O2 = 6.0 were selected as targets for
modification. Then, ML-aided investigation of an additive fourth element
as a promoter was performed at the SVR field based on the HTS database
among 350 catalysts (40,330 data points). Application of one-hot encoding
to ascertain positive elements for CH4 conversion revealed
that manganese (Mn) frequently appears at CH4 conversion
higher than 44.0%. After the 10 selected catalysts were prepared with
the Mn additive, their OCM performance was compared with those of
pristine three-element-supported catalysts. Results show that four
catalysts represent positive features on C2 yield in the
presence of additive Mn working as a promoter. Consequently, 5 wt
% Mn-loaded LiFeBa/La2O3 and LiBaLa/La2O3, respectively, show attractive OCM performance of 16.3%
C2 yield with 88.4% selectivity and 13.8% C2 yield with 71.9% selectivity, even under an O2-lean condition
(CH4/O2 = 6.0).