Machine-Learning-Enabled Structural Design and Optimization of Methanol Reforming Hydrogen Reactors

Abstract

A methanol steam reforming and combustion channel reactor was developed to supply hydrogen for high-power fuel cells. Numerical simulation combined with a backpropagation (BP) neural network, whose maximum error is below 5 %, quantified how channel dimensions and inlet flows influence peak temperature difference, flow maldistribution, methanol conversion, and hydrogen output. Multi-objective optimization using non-dominated sorting genetic algorithm II (NSGA–II) yielded the optimal layout: length 284 mm, width 171 mm, combustor inlet flow 8.3 × 10³ mL min⁻¹, and reformer inlet flow 4.7 × 10⁴ mL min⁻¹. Compared with the initial design, the optimized reactor reduces peak temperature and flow differences by 6.6 % and increases methanol conversion and hydrogen production by about 7 %, meeting the hydrogen demand of roughly 23 kW fuel cells.