Investigation of a solar-assisted methanol steam reforming system: Operational factor screening and computational fluid dynamics data-driven prediction

Abstract

The development of solar-to-fuel technologies has received significant attention while facing the growing interest in clean energy production. To accurately evaluate and optimize the solar-assisted thermochemical hydrogen production process, a reliable prediction method that ensures stable accuracy is essential. We utilized a gray correlation analysis (GRA) and a multi-objective genetic algorithm (GA) model to optimize the operating parameters of a methanol steam reforming system. Computational fluid dynamics simulations provided the response values for key parameters, including reactant conversion rate, syngas product yield, and CO selectivity. Additionally, the Taguchi’s method and analysis of variance (ANOVA) were employed to assess the impact of operating parameters on these target outputs. The results demonstrate that the proposed GA-Back propagation neural networks (GA-BPNN) model significantly outperforms traditional prediction models in accuracy, exhibiting robustness and generalization. The mean absolute percentage error (MAPE) of methanol conversion, hydrogen yield, and CO selectivity is 3.20 %, 4.69 % and 3.53 %. The GRA-GA-BPNN model shows a slight decline, while still maintains reliable prediction accuracy, with the MAPE value of 5.76 %, 5.11 %, and 10.4 %. This operational factor screening-based prediction model proves useful for large-scale data-driven predictions of solar thermochemical systems, especially under complex and variable external conditions and internal human interventions.