Optimization of Vacuum Drying Properties for Chlorococcum infusionum Microalgae Moisture Content Using Hybrid Genetic Programming and Genetic Algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Mendigoria:2021:HNICEM,
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author = "Christan Hail Mendigoria and Ronnie Concepcion and
Ryan Rhay Vicerra and Andres Philip Mayol and
Alvin Culaba and Elmer Dadios and Argel Bandala",
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booktitle = "2021 IEEE 13th International Conference on Humanoid,
Nanotechnology, Information Technology, Communication
and Control, Environment, and Management (HNICEM)",
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title = "Optimization of Vacuum Drying Properties for
Chlorococcum infusionum Microalgae Moisture Content
Using Hybrid Genetic Programming and Genetic
Algorithm",
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year = "2021",
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month = "28-30 " # nov,
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address = "Manila, Philippines",
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keywords = "genetic algorithms, genetic programming",
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isbn13 = "978-1-6654-0168-5",
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DOI = "doi:10.1109/HNICEM54116.2021.9732016",
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abstract = "Biofuel production serves as a viable alternative to
conventional energy production systems which primarily
relies on fossil fuels. Because of its increased
protein and lipid accumulation properties, algal
biomass has been deemed a feasible source for biofuel
generation among the many types of biomass materials.
Microalgal drying process, a preliminary process prior
to biofuel production, is a crucial procedure which
consumes a lot of energy. Thus, optimization of this
process must be considered. As a response, this study
aims to determine the optimal vacuum drying parameters
such as the biomass thickness, drying temperature and
vacuum pressure in reference to the moisture content of
the microalgae, Chlorococcum infusionum, using hybrid
evolutionary strategies of genetic programming (GP) and
genetic algorithm (GA). GP was configured using the
GPTIPSv2 tool to generate a symbolic function which is
a fundamental element of GA optimization. GA was used
to generate candidate solutions which were evaluated
for goodness of fit through the developed function.
Based on the results, this optimization generated
parameter values of 5 mm, 69.4degreeC, and 178.3 mbar
for biomass thickness, temperature, and pressure,
respectively, which converges at the function value
of121.344. This developed technique served as a
non-invasive optimization model to computationally
determine the optimal microalgal drying parameter
values.",
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notes = "Also known as \cite{9732016}",
- }
Genetic Programming entries for
Christan Hail Mendigoria
Ronnie S Concepcion II
Ryan Rhay P Vicerra
Andres Philip Mayol
Alvin Culaba
Elmer Jose P Dadios
Argel A Bandala
Citations