Adsorptive
desulfurization (ADS) of hydrocarbon fuels using zeolite-based
adsorbents holds great promise due to the mild conditions required
to remove sulfur, thus addressing the energy and environmental concerns.
However, screening of the ever-increasing number of potential ADS
zeolites for adsorptive capacity is increasingly intractable. Furthermore,
there is no consensus on the parameters with a dominating influence;
hence, adsorbent synthesis design has remained an art. Machine learning
(ML) has gained popularity as a powerful tool for understanding the
catalytic mechanism and providing insights into catalytic design.
In this study, we used multiple linear regression (MLR) and random
forest (RF) regression to explore the process of ADS by zeolites using
data from the literature. We found better predictive performance under
the RF model (R
2 = 0.93) than the MLR
model (R
2 = 0.88), which violated the
assumption of linearity. The initial adsorbate concentration showed
the highest relative importance of the variables, followed by zeolite
properties (metal ion, mesoporous volume, pore size, Si/Al ratio,
and surface area) for ADS activity. Our RF prediction model may be
used in place of experimental ADS zeolite screening, cutting down
on time and resource requirements. This work demonstrates the utility
of ML and literature survey data as an inexpensive alternative to
experimentation when doing research to obtain mechanistic insight
into the complex process of ADS.