Article ID Journal Published Year Pages File Type
381753 Engineering Applications of Artificial Intelligence 2007 11 Pages PDF
Abstract

Artificial neural network (ANN) approach was used to design an optimum Ni/Al2O3 catalyst for the production of hydrogen by the catalytic reforming of crude ethanol based on determining the inter-relationships between catalyst-preparation methods, nickel loading, catalyst characteristics and catalyst performance. ANN could predict hydrogen production performance of various Ni/Al2O3 catalysts of various elemental compositions and methods of preparation in the production of hydrogen by the catalytic reforming of crude ethanol in terms of crude-ethanol conversion, hydrogen selectivity and hydrogen yield. Specifically on catalyst design, ANN was used to determine the optimum catalyst conditions for obtaining maximum hydrogen production performance of a Ni/Al2O3 catalyst for the production of hydrogen by the catalytic reforming of crude ethanol. The optimal hydrogen yield was 4.4 mol %, and the associated crude-ethanol conversion and H2 selectivity for the optimal hydrogen yield were 79.6 and 91.4 mol%, respectively. The optimal catalyst was the one prepared by the coprecipitation method with the optimal nickel loading of 12.4 wt% and an optimal aluminum composition of 42.5 wt%.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
Authors
, , , ,