  We accelerate reactive transport (RT) simulation by replacing the geochemical
solver in the RT code by a surrogate model or emulator, considering either a
trained deep neural network (DNN) or a k-nearest neighbor (kNN) regressor. We
focus on 2D leaching of hardened cement paste under diffusive or
advective-dispersive transport conditions, a solid solution representation of
the calcium silicate hydrates and either 4 or 7 chemical components, and use
the HPx (coupled Hydrus-PHREEQC model) reactive transport code as baseline. We
find that after training, both our DNN-based and kNN-based codes, HPx-DNN and
HPx-kNN, can make satisfactorily to very accurate predictions while providing
either a 3 to 9 speedup factor compared to HPx with parallelized geochemical
calculations over 4 cores. Benchmarking against single-threaded HPx, these
speedup factors become 8 to 33. Overall, HPx-DNN are HPx-kNN are found to
achieve a close to optimal speedup when DNN regression and kNN search are
performed on a GPU. Importantly, for the more complex 7-components cement
system, no emulator that is globally accurate over the full space of possible
geochemical conditions could be devised. Instead we therefore build "local"
emulators that are only valid over a relevant fraction of the input parameter
space. This space is identified by running a coarse and thus computationally
cheap full RT simulation, and subsequently explored by kernel density sampling.
Future work will focus on improving accuracy for this type of cement systems.
