Kinetic-model identification in metal-hydride reactions using neural network autoencoder surrogate models

Solid-state hydrides can reversibly absorb and desorb H₂ under comparatively mild temperature and pressure conditions, making them promising candidates for H₂ storage in renewable energy applications. The underlying gas-solid interactions are complex and involve multiple intermediary steps. Because they occur in series, by fitting experimental data employing several proposed models, it is possible to identify the rate-limiting step of the reaction, driving the development of new catalysts and the design of H₂-storage systems. The corresponding state-of-the-art method for model identification is the reduced-time method (RTM), which is time-consuming and often yields inconclusive results. To overcome these limitations and to facilitate automatization, this work proposes a framework with 12 unsupervised neural networks (NNs) which are trained using simulated curves from selected kinetic models. These networks are applied to a dataset of 144 experimental kinetic curves of an AB₂ hydride-forming alloy as a blueprint material. Each NN attempts to reconstruct the input data, and the model with the lowest reconstruction loss is selected. The machine learning algorithm achieved a match of 97% and 91% for the absorption/desorption curves compared to the benchmark. Both reactions follow predominantly the Avrami-Erofeyev model with exponents (n) between 0.8 and 0.9. The kinetic constants (k) derived from the assigned model are used to simulate kinetic curves, showing excellent agreement with experimental data and RTM results. The proposed method provides an advantageous approach that can be applied to most gas-solid or even solid-solid reactions.

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