A Multi-Output Machine Learning Approach to Sorption-Enhanced Steam Methane Reforming
DOI:
https://doi.org/10.33736/jaspe.11153.2026Keywords:
Hydrogen production, Carbon capture, Machine learning, Methane conversion, EnergyAbstract
Sorption-enhanced steam methane reforming (SE-SMR) provides the added advantage of CO2 capture compared to conventional steam methane reforming, limited by CO2 emissions. Current SE-SMR machine learning models primarily focus on predicting methane conversion and hydrogen production, but overlook carbon dioxide capture and hydrogen purity. This limitation reduces their effectiveness for real-time applications, where it is crucial to balance trade-offs involving environmental, economic, and technical objectives. This research employs a dual multi-output framework to predict methane conversion, hydrogen yield, hydrogen purity, and carbon dioxide capture using Linear Regression, Support Vector Machine, Decision Tree, Random Forest, CatBoost, and LightGBM models. Most models demonstrated high prediction accuracy, characterised by high R² and low root mean square error (RMSE) values. Support Vector Machine (SVM) performed the best in predicting methane conversion, hydrogen yield, and hydrogen purity, achieving R² values of 0.993, 0.988, and 0.979, respectively. Random forest performed well in all models, but achieved its best results in predicting carbon dioxide capture, with an R² value of 0.996 and an RMSE of 2.059%. This study presents a reliable technique for predicting hydrogen production from SE-SMR integrated with carbon dioxide capture.
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