Paper Info
Title
An evolutionary computation approach to predicting output voltage from fuel utilization in SOFC stacks
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 utilization behavior. This method is shown to outperform the state-of-the-art radial basis function neural network approach for SOFC modeling.
Year
DOI
Venue
2009
10.1109/CEC.2009.4983209
IEEE Congress on Evolutionary Computation
Keywords
Field
DocType
nonlinear dynamic,neural network approach,sofc output voltage,solid oxide fuel cell,efficient genetic programming approach,complicated system,sofc stack,fuel utilization behavior,powerful approach,sofc modeling,stack-based system,evolutionary computation approach,solid modeling,power generation,evolutionary computing,evolutionary computation,voltage,modeling and simulation,predictive models,nonlinear dynamics,context modeling,genetic programming,steady state,system of equations,mathematical model,genetic algorithms
Mathematical optimization,Nonlinear system,Stack (abstract data type),Computer science,Modeling and simulation,Voltage,Evolutionary computation,Genetic programming,Control engineering,Genetic algorithm,Solid oxide fuel cell
Conference
Citations 
PageRank 
References 
0
0.34
2
Authors
1
Name
Order
Citations
PageRank
Uday K. Chakraborty187344.61