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:1.8051

@InProceedings{Mendigoria:2021:HNICEM,

• author = "Christan Hail Mendigoria and Ronnie Concepcion and Ryan Rhay Vicerra and Andres Philip Mayol and Alvin Culaba and Elmer Dadios and Argel Bandala",
• booktitle = "2021 IEEE 13th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management (HNICEM)",
• title = "Optimization of Vacuum Drying Properties for Chlorococcum infusionum Microalgae Moisture Content Using Hybrid Genetic Programming and Genetic Algorithm",
• year = "2021",
• month = "28-30 " # nov,
• address = "Manila, Philippines",
• keywords = "genetic algorithms, genetic programming",
• isbn13 = "978-1-6654-0168-5",
• DOI = "doi:10.1109/HNICEM54116.2021.9732016",
• 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.",
• 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