Neural Optimization of Axial- and Radial-Flow Microreactors for Hydrogen Production by Steam Reforming of Methanol

Abstract

The reforming process between methanol and steam is an important source of hydrogen. Since conventional tubular reactors require storage and transport facilities, which are risk-prone, microreactors are being investigated as convenient portable sources for in situ generation of hydrogen. A recent study has shown that radial-flow microreactors are more efficient than conventional axial-flow designs. These comparisons were made through mathematical models. Since equation-oriented models are of limited validity under realistic conditions, a neural network approach has been used here to model the process and maximize hydrogen production. By optimizing through a recurrent Elman network, the performance of the radial-flow reactor is enhanced by 20%–30%; larger improvements were seen for the axial-flow geometry, but the overall production of hydrogen was still lower than with radial flow. Based on these results and previous work, further improvements may still be possible through hybrid neural models.