Abstract:
The depletion of conventional fuels and the urgent need for clean and sustainable energy
sources have driven research into H2 production through catalytic methane decomposition.
This study aimed to use novel machine learning (ML) approaches to enhance and predict H2
yield using Artificial Neural Network (ANN), Ensembled Tree (ET), Gaussian Process
Regression (GPR), Regression Tree (RT), and Support Vector Machine (SVM). A two-step
approach involving feature selection and hyperparameter optimization was employed to
enhance the models' performance for H2 yield. The coefficient of correlation (R2
) and Root
Mean Square Error (RMSE) were used to evaluate model performance. ET performed
excellent with R2
of 0.929 (training) and 0.933 (testing) however GPR exhibited exceptional
performance, achieving a perfect training R2
of 1.00 and low RMSE 0.00026. Furthermore,
partial dependence plots (PDPs) were utilized to assess the impact of catalyst properties and
reaction conditions on H2 yield. Temperature impacts H2 directly, while time shows an
inverse relationship. Various catalysts and catalysts structure exhibited distinct behaviors, and
the Average surface area demonstrated a direct linear relationship with H2 yield. These
findings contribute to the understanding of catalytic methane decomposition and provide
insights for optimizing H2 production processes using ML models. Given the depletion of
conventional fuels, H2 has emerged as a crucial alternative energy source due to its clean and
sustainable nature. The ability to accurately predict H2 yield using ML models opens new
avenues for advancing H2 production technologies and meeting the growing global energy
demand while mitigating environmental concerns.
