Framework based on number of basis functions complexity measure in investigation of the power characteristics of direct methanol fuel cell

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@Article{Garg:2016:CILS,

• author = "Akhil Garg and B. N. Panda and D. Y. Zhao and K. Tai",
• title = "Framework based on number of basis functions complexity measure in investigation of the power characteristics of direct methanol fuel cell",
• journal = "Chemometrics and Intelligent Laboratory Systems",
• volume = "155",
• pages = "7--18",
• year = "2016",
• ISSN = "0169-7439",
• DOI = "doi:10.1016/j.chemolab.2016.03.025",
• URL = "http://www.sciencedirect.com/science/article/pii/S0169743916300612",
• abstract = "A potential alternative to cell batteries is the air-breathing micro direct methanol fuel cell (muDMFC) because it is environmental friendly, charging-free, possesses high energy density properties and provides easy storage of the fuel. The effective functioning of the complex air-breathing uDMFC system exhibits higher dependence on its operating conditions and the parameters. The main challenge for the experts is to determine its optimum operating conditions. In this context, the mathematical modelling approach based on evolutionary framework of genetic programming (GP) can be applied. However, its successful implementation depends on the complexity chosen in its structural risk minimization (SRM) objective function. In this work, the two measures of complexity based on the standardized number of nodes and the number of basis functions in the splines is chosen. Comparison between the two GP approaches based on these two complexity measures is evaluated on the experimental procedure performed on the DMFC. The power characteristics considered in this study are power density and open-circuit voltage and the three inputs considered are methanol flow rate, methanol concentration and the cell temperature. The statistical analysis based on cross-validation, error metrics and hypothesis tests is performed to choose the best GP based power characteristics models. Further, 2-D plots for measuring the individual effects and the 3-D plots for the interaction effects of the inputs on the power characteristics is plotted based on the parametric approach. It was found that the methanol concentration influences the power characteristics (power density and OCV) of DMFC the most followed by cell temperature and methanol flow rate.",
• keywords = "genetic algorithms, genetic programming, Direct methanol fuel cell, DFMC, Fuel cell performance, Power characteristics",

}

Genetic Programming entries for Akhil Garg Biranchi Narayan Panda D Y Zhao Kang Tai

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