Catalytic
methylation of aromatic hydrocarbons using CO2 and H2 as a methylating agent was conducted over a combination
of TiO2-supported Re (Re(1)/TiO2; Re = 1 wt
%) and H-β (SiO2/Al2O3 = 40)
in a batch reactor. Catalytic methylation of m-xylene
was performed, and this catalyst combination demonstrated excellent
performance for the synthesis of methylbenzenes, giving a high yield
of total methylated products (10 and 57%, as calculated on the basis
of CO2 and m-xylene, respectively), while
generating relatively small amounts of byproducts such as demethylated
and dearomatized products as well as CO and CH4 in the
gas phase under the investigated reaction conditions (p
CO2
= 1 MPa, p
H2
= 5 MPa, T = 240 °C, t = 20 h). Our catalysts were also found to perform well for the methylation
of toluene, providing a high yield and high selectivity for methylated
products compared with the other investigated catalyst combinations.
In addition to conducting conventional-type catalyst research, we
used a data science approach based on machine learning techniques
to identify important input variables that govern the catalytic performance,
enabling optimization of the catalyst for the methylation reaction.
Compared with the catalysts optimized using the conventional approach,
the improved Re/TiO2 catalyst with a Re loading amount
of 1.8 wt %, which was optimized with the aid of ML, exhibited greater
activity toward the methylation of benzene using CO2/H2.