  This work investigates the performance of the on-demand machine learning
(ODML) algorithm introduced in Leal et al. (2020) when applied to different
reactive transport problems in heterogeneous porous media. ODML was devised to
accelerate the computationally expensive geochemical reaction calculations in
reactive transport simulations. We demonstrate that the ODML algorithm speeds
up these calculations by one to three orders of magnitude. Such acceleration,
in turn, significantly accelerates the entire reactive transport simulation.
The numerical experiments are performed by implementing the coupling of two
open-source software packages: Reaktoro (Leal, 2015) and Firedrake (Rathgeber
et al., 2016).
