AI-Driven Catalyst Optimization in Methane Steam Reforming: A Hybrid HGBO–VIKOR and ConvLSTM Framework for Sustainable Hydrogen Production

Methane steam reforming (MSR) is the most widely used industrial process for hydrogen production. However, catalyst deactivation, carbon emissions, and energy inefficiencies limit its sustainable performance. Therefore, improving catalyst selection and optimizing operating conditions are essential for efficient hydrogen generation. This study proposes an artificial intelligence-driven framework to optimize catalyst–condition combinations in MSR systems. The framework integrates Hybrid Golden Beetle Optimization (HGBO), VIKOR-based multi-criteria decision making, and Convolutional Long Short-Term Memory (ConvLSTM) modeling. HGBO explores the solution space and generates Pareto-optimal combinations of catalysts and operating conditions. These solutions are then ranked using the VIKOR method. The ranking considers hydrogen yield, methane conversion, energy efficiency, CO 2 emissions, and catalyst lifetime. Economic feasibility is also included in the decision process. ConvLSTM modeling captures spatiotemporal relationships in catalyst and process data and predicts catalyst degradation under different operating conditions. The framework is evaluated using 620 experimentally reported MSR cases collected from the published literature within industrial ranges of 600–1200 °C, 1–40 bar, and H 2 O/CH 4 ratios of 1–6. The optimized configurations achieve hydrogen yields up to 98.5%, energy efficiency approaching 99%, and reduced CO 2 emissions of about 0.85 kg h −1 . The results provide practical guidance for catalyst selection and process optimization in industrial hydrogen production systems.