Predictive model of algal biofuel production based on experimental data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{AZARI:2020:AR,
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author = "Aryandokht Azari and Hossein Tavakoli and
Brian D. Barkdoll and Omid Bozorg Haddad",
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title = "Predictive model of algal biofuel production based on
experimental data",
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journal = "Algal Research",
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volume = "47",
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pages = "101843",
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year = "2020",
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ISSN = "2211-9264",
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DOI = "doi:10.1016/j.algal.2020.101843",
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URL = "http://www.sciencedirect.com/science/article/pii/S2211926419309087",
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keywords = "genetic algorithms, genetic programming, Bioenergy,
Algae, Optimization, CO biofixation rate,
Photobioreactors",
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abstract = "Algal biofuels are of growing interest in the quest to
reduce carbon emissions in the atmosphere but the
sensitivity of the fuel production to various factors
is not well understood. Therefore, the effects of
temperature, light intensity, carbon concentration,
aeration rate, pH, and time on the CO2 biofixation rate
of Chlorella vulgaris (ISC-23) were investigated using
experimental, and Genetic Programming (GP) modeling
techniques. The impacts of applying the cement
industrial flue gas as a source of carbon, useful for
the growth of microalgae, were also studied. Chlorella
vulgaris (ISC-23) was cultivated in a laboratory
photobioreactor on a BG-11 medium. The developed GP
model was used to optimize the CO2 biofixation based on
the studied variables and produce a predictive
equation. By using statistical measurements and error
analysis, the predictive equation was shown to agree
with the experimentally obtained values. It was found
that the optimum conditions occur at 26o C, and 3200 lx
of light, in the existence of CO2. Applying 6percent
CO2 as the input with the aeration rate of 0.5 vvm in
11 days was also reported as the optimum scenario for
algae production with keeping the pH close to 7.5. The
results indicate that the predictions determined with
the proposed equation can be of practical worth for
researchers and experts in the biofuel industry",
- }
Genetic Programming entries for
Aryandokht Azari
Hossein Tavakoli
Brian D Barkdoll
Omid Bozorg Haddad
Citations