- author = "Uday K. Chakraborty",
- title = "An Evolutionary Computation Approach to Predicting Output Voltage from Fuel Utilization in {SOFC} Stacks",
- booktitle = "2009 IEEE Congress on Evolutionary Computation",
- year = "2009",
- editor = "Andy Tyrrell",
- pages = "2165--2171",
- address = "Trondheim, Norway",
- month = "18-21 " # may,
- organization = "IEEE Computational Intelligence Society",
- publisher = "IEEE Press",
- isbn13 = "978-1-4244-2959-2",
- file = "P686.pdf",
-
DOI = "
doi:10.1109/CEC.2009.4983209",
- size = "7 pages",
- abstract = "Modeling of solid oxide fuel cell (SOFC) stack based systems is a powerful approach that can provide useful insights into the nonlinear dynamics of the system without the need for formulating complicated systems of equations describing the electrochemical and thermal properties. This paper presents an efficient genetic programming approach for modeling and simulation of SOFC output voltage versus fuel burn behavior. This method is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling.",
- keywords = "genetic algorithms, genetic programming, RBFANN",
-
notes = "Fuel cell hydrogen + oxygen = steam + 1.18volts at
1000Centigrade and 1bar. DSS \cite{ga94aGathercole}
Discipulus. NeuroSolutions.
CEC 2009 - A joint meeting of the IEEE, the EPS and the IET. IEEE Catalog Number: CFP09ICE-CDR",
