Impact of Hyperparameters on Surrogate Model Performance: Calcite Dissolution Under Geological Disposal Conditions
Year: 2026
Type: article
Abstract: Efficient simulation of geochemical reactions is critical for predicting the long-term chemical evolution of geological disposal repositories for radioactive waste. In large-scale reactive transport simulations, geochemical equilibrium calculations often represent a major computational bottleneck because they must be repeatedly solved for many spatial cells and time steps. This study investigates the development of machine-learning-based surrogate models that are designed to approximate geochemical equilibrium calculations and thereby significantly accelerate reactive transport simulations while reducing computational resource requirements. Calcite dissolution induced by magnesium-rich fluid inflow is used as a representative test case to evaluate the feasibility and performance of such surrogate models. Training and validation datasets were generated using the IPhreeqc C++ API, enabling the automated execution of a large number of PHREEQC equilibrium simulations across a chemically relevant parameter space. The resulting dataset captures nonlinear relationships between initial aqueous composition and outputs of interest after chemical equilibration, including aqueous species concentrations and amounts of minerals. Fully connected feed-forward neural networks were designed and implemented in TensorFlow to reproduce PHREEQC results, and the influence of key hyperparameters—such as network depth, width, activation functions, learning rate, and batch size—was systematically investigated. The results demonstrate that surrogate model accuracy and training stability are sensitive to hyperparameter selection, even for a relatively simple chemical system. Properly configured neural network architectures reproduce equilibrium geochemical responses with high accuracy and provide a computationally efficient alternative to repeated PHREEQC calculations, highlighting their potential for accelerating large-scale reactive transport modelling workflows.
Source: Applied Sciences
DOI: 10.3390/app16094252
Institution: Lithuanian Energy Institute
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W7158321618