Adaptive Neural Network Control of Hydrogen Production via Autothermal Reforming
of Methanol
Abstract:
This study focuses on the control problem of hydrogen production via autothermal reforming of methanol. To deal with uncertain system dynamics and external disturbance, an improved adaptive neural network controller is designed to regulate hydrogen flow rate by manipulating methanol flow rate. Theoretical derivation and analysis demonstrate its adaptability to model mismatch and external disturbance. Furthermore, a variable ratio controller law is employed as the reforming temperature controller to achieve steady reforming temperature by adjusting the reforming air flow rate. Finally, the effectiveness of the entire system is testified by experimental means.
How to cite this article
Zhuang Hong, Lu Jian-Gang, Yang Qin-Min, Wang Xue-Fei and Chen Jin-Shui, 2013. Adaptive Neural Network Control of Hydrogen Production via Autothermal Reforming
of Methanol. Information Technology Journal, 12: 6992-6997.
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