A neural network model in LaNixAl1-xO3 catalyst for methane reforming in SOFC

Sian Jie Ciou, Ya Wun Jhang, Ying Jhih Lai, Kuan Zong Fung, Min Hsiung Hung, Kai Wei Chiang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


There has been a great interest in application of SOFC using hydrocarbon fuel, and the major issue associated with the improvement of steam reforming is the possibility to extend the area of operation conditions, and lead to low carbon formation. The improvement can be achieved by modifying the design of existing nickel catalysts. Generally, mathematical modeling can be considered as an important element in the methane reforming in terms of the avoidance of extensive experiments. In this article, a two layered feed-forward neural network has been trained with the back propagation algorithm to learn parameters in the process that the LaNixAl1-xO3(x = 0.1-0.9), reforms methane. The data used during the training procedure are generated by using of mass spectrometer after reforming. The average values of the errors for prediction are well below 5% The artificial neural network (ANN) model was capable of modeling the relationship between catalytic behaviors and the structure of catalytic well.

Original languageEnglish
Title of host publicationECS Transactions - 10th International Symposium on Solid Oxide Fuel Cells, SOFC-X
Number of pages9
Edition1 PART 2
Publication statusPublished - 2007
Event10th International Symposium on Solid Oxide Fuel Cells, SOFC-X - , Japan
Duration: 2007 Jun 32007 Jun 8

Publication series

NameECS Transactions
Number1 PART 2
ISSN (Print)1938-5862
ISSN (Electronic)1938-6737


Other10th International Symposium on Solid Oxide Fuel Cells, SOFC-X

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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