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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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/6/1/12 |
| _version_ | 1827851968871137280 |
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| author | Jaya Shankar Tumuluru Dean J. Heikkila |
| author_facet | Jaya Shankar Tumuluru Dean J. Heikkila |
| author_sort | Jaya Shankar Tumuluru |
| collection | DOAJ |
| description | 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.). |
| first_indexed | 2024-03-12T10:42:24Z |
| format | Article |
| id | doaj.art-278f6541871b43a68598945ff3290f4e |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-12T10:42:24Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-278f6541871b43a68598945ff3290f4e2023-09-02T08:00:19ZengMDPI AGBioengineering2306-53542019-01-01611210.3390/bioengineering6010012bioengineering6010012Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic AlgorithmJaya Shankar Tumuluru0Dean J. Heikkila1Idaho National Laboratory, 750 MK Simpson Blvd., Energy Systems Laboratory, P.O. Box: 1625, Idaho Falls, ID 83415-3570, USAIntern, University of Washington, 1410 NE Campus Parkway, Seattle, WA 98195, USABiomass 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.).https://www.mdpi.com/2306-5354/6/1/12renewable energycorn stovergrinding processoptimizationresponse surface methodologyhybrid genetic algorithm |
| spellingShingle | Jaya Shankar Tumuluru Dean J. Heikkila Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm Bioengineering renewable energy corn stover grinding process optimization response surface methodology hybrid genetic algorithm |
| title | Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm |
| title_full | Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm |
| title_fullStr | Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm |
| title_full_unstemmed | Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm |
| title_short | Biomass Grinding Process Optimization Using Response Surface Methodology and a Hybrid Genetic Algorithm |
| title_sort | biomass grinding process optimization using response surface methodology and a hybrid genetic algorithm |
| topic | renewable energy corn stover grinding process optimization response surface methodology hybrid genetic algorithm |
| url | https://www.mdpi.com/2306-5354/6/1/12 |
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