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

Tumuluru, Jaya Shankar; Heikkila, Dean J.

doi:10.3390/bioengineering6010012

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

Journal Article · 24 January 2019 · Bioengineering

DOI: https://doi.org/10.3390/bioengineering6010012 · OSTI ID:1494676

^[1]; Heikkila, Dean J. ^[2]

Idaho National Lab. (INL), Idaho Falls, ID (United States)
Univ. of Washington, Seattle, WA (United States)

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.).

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Research Organization:: Idaho National Laboratory (INL), Idaho Falls, ID (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC07-05ID14517

OSTI ID:: 1494676

Report Number(s):: INL/JOU--18-52333-Rev000

Journal Information:: Bioengineering, Journal Name: Bioengineering Journal Issue: 1 Vol. 6; ISSN 2306-5354; ISSN BIOEBG

Publisher:: MDPICopyright Statement

Country of Publication:: United States

Language:: English

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37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
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Title: Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm

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References (49)

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