Impacts of process parameters on diesel reforming via interpretable machine learning

Diesel reforming is a promising hydrogen production technology used for the clean energy conversion of high-carbon-content fuels. Although the reaction system has been established, predicting the optimal reaction conditions for the system remains challenging. Here, we obtained a set of 675 data points from Aspen Plus simulations and trained regression models to predict the reaction condition ranges that yield the highest hydrogen production in diesel reforming. The ETR model achieved the best predictive performance, with an R-2 value of 0.99. Interpretable machine learning methods revealed that temperature is a crucial feature determining the baseline hydrogen yield of the diesel reforming reaction, while the steam-to-carbon ratio is key to enhancing hydrogen yield. Our exploratory study underscores the ability of data-driven ML models to uncover the condition-yield relationship in catalytic diesel reforming for hydrogen production by isolating the effects of individual design parameters, a feat that is difficult to achieve through experimental means.