Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm

Biomass could be a key source of renewable energy. Agricultural waste products, such as corn stover, provide a convenient means to replace fossil fuels, such as coal, and a large amount of feedstock is currently available for energy consumption in the U.S. This study has two main objectives: (1) to...

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Bibliographic Details
Published in:Bioengineering (Basel) 2019-01, Vol.6 (1), p.12
Main Authors: Tumuluru, Jaya Shankar, Heikkila, Dean J
Format: Article
Language:English
Subjects:
09 BIOMASS FUELS
Agricultural wastes
Algorithms
Biodiesel fuels
Bioengineering
Biofuels
Biomass
Bulk density
Conversion
Corn
corn stover
Design of experiments
Energy consumption
Fossil fuels
genetic algorithm
Genetic algorithms
grinding process
hybrid genetic algorithm
INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Lignocellulose
lignocellulosic substances
Literature reviews
Moisture content
Optimization
Particle size
Physical properties
Raw materials
renewable energy
Response surface methodology
Variables
Water content
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Summary:Biomass could be a key source of renewable energy. Agricultural waste products, such as corn stover, provide a convenient means to replace fossil fuels, such as coal, and a large amount of feedstock is currently available for energy consumption in the U.S. This study has two main objectives: (1) to understand the impact of corn stover moisture content and grinder speed on grind physical properties; and (2) develop response surface models and optimize these models using a hybrid genetic algorithm. The response surface models developed were used to draw surface plots to understand the interaction effects of the corn stover grind moisture content and grinder speed on the grind physical properties and specific energy consumption. The surface plots indicated that a higher corn stover grind moisture content and grinder speed had a positive effect on the bulk and tapped density. The final grind moisture content was highly influenced by the initial moisture content of the corn stover grind. Optimization of the response surface models using the hybrid genetic algorithm indicated that moisture content in the range of 17 to 19% (w.b.) and a grinder speed of 47 to 49 Hz maximized the bulk and tapped density and minimized the geomantic mean particle length. The specific energy consumption was minimized when the grinder speed was about 20 Hz and the corn stover grind moisture content was about 10% (w.b.).
ISSN:2306-5354
2306-5354
DOI:10.3390/bioengineering6010012