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 × 10 3 mL min −1 , and reformer inlet flow 4.7 × 10 4 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.