Design of explicit models for estimating efficiency characteristics of microbial fuel cells
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- @Article{GARG:2017:Energy,
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author = "A. Garg and Jasmine Siu Lee Lam",
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title = "Design of explicit models for estimating efficiency
characteristics of microbial fuel cells",
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journal = "Energy",
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volume = "134",
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pages = "136--156",
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year = "2017",
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keywords = "genetic algorithms, genetic programming, Microbial
fuel cell, MFC features modelling, MFC features
prediction, Fuel cell modelling, Microbial microfluidic
cell, Computational intelligence",
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ISSN = "0360-5442",
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DOI = "doi:10.1016/j.energy.2017.05.180",
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URL = "http://www.sciencedirect.com/science/article/pii/S0360544217308770",
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abstract = "Recent years have seen the use of microbial fuel cells
for the generation of electricity from wastewater and
renewable biomass. The efficiency characteristics
(power density and voltage output) of fuel cells depend
highly on their operating conditions such as current
density, chemical oxygen demand concentration and
anolyte concentration. Computational intelligence
methods based on genetic programming and multi-adaptive
regression splines are proposed in design of explicit
models for estimating efficiency characteristics of
microfluidic microbial fuel cells based on the
operating conditions. Performance of the models
evaluated against the actual data reveals that the
models formulated from genetic programming outperform
the multi-adaptive regression splines models. The
robustness in the best models is validated by
performing simulation of the models over 8000 runs
based on the normal distribution of the operating
conditions. 2-D and 3-D surface analysis conducted on
the models reveals that the power density of the fuel
cell increases with an increase in values of chemical
oxygen demand concentration and current density till a
certain value and then decreases. The voltage output
decreases with an increase in values of current density
while increases with an increase in values of chemical
oxygen demand concentration to a certain limit",
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keywords = "genetic algorithms, genetic programming, Microbial
fuel cell, MFC features modelling, MFC features
prediction, Fuel cell modelling, Microbial microfluidic
cell, Computational intelligence",
- }
Genetic Programming entries for
Akhil Garg
Jasmine Siu Lee Lam
Citations