Diffusion-induced gas separation
is crucial for industrial
applications,
while the determination of specific conditions is still challenging.
Here, molecular dynamics simulation data were used to train machine
learning models to identify the effective separation conditions for
blast furnace gas confined in nanosilts with different absorption
strengths (graphene and graphene oxide). The diffusion coefficients
and exponents of the blast furnace gas were obtained as a database
by molecular dynamics (MD) simulations. Several environmental and
structural controlling factors (such as temperature, layer distance,
atomic number, ionization potential, etc.) were extracted through
importance analysis. And the relationships between these factors and
diffusion properties were further established by the Kernel Ridge
Regression algorithm. Based on the differences in diffusion coefficients,
specific binary gas mixtures in the blast furnace gas with competitively
high separation potential have been screened out by a trained machine
learning model and verified by MD simulations. The simulation strategy
can provide theoretical guidance for the structural design of membranes
for diffusion-controlled gas separation.