AIMS Bioengineering, 2016, 3(2): 125-138. doi: 10.3934/bioeng.2016.2.125.

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Impact of an innovated storage technology on the quality of preprocessed switchgrass bales

1 Department of Agricultural and Resource Economics, University of Tennessee, 302-I Morgan Hall, Knoxville, TN 37996, USA
2 Center for Renewable Carbon, University of Tennessee, 2506 Jacob Drive, Knoxville, TN 37996, USA

The purpose of this study was to determine the effects of three particle sizes of feedstock and two types of novel bale wraps on the quality of switchgrass by monitoring the chemical changes in cellulose, hemicellulose, lignin, extractives, and ash over a 225-day period. Using NIR (Near-infrared) modeling to predict the chemical composition of the treated biomass, differences were found in cellulose, lignin, and ash content across switchgrass bales with different particle sizes. Enclosing bales in a net and film impacted the cellulose, lignin, and ash content. Cellulose, hemicellulose, lignin, extractives, and ash were different across the 225-day storage period. A quadratic response function made better prediction about cellulose, lignin, and ash response to storage, and a linear response function best described hemicellulose and extractives response to storage. This study yields valuable information regarding the quality of switchgrass at different intervals between the start and end date of storage, which is important to conversion facilities when determining optimal storage strategies to improve quality of the biomass feedstock, based on potential output yield of a bale over time.
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Keywords chemical composition; near-infrared spectroscopy; lignocellulosic biomass quality; particle size; preprocessing; storage; switchgrass

Citation: Christopher N. Boyer, T. Edward Yu, Burton C. English, James A. Larson, Nicole Labbé, Lindsey M. Kline. Impact of an innovated storage technology on the quality of preprocessed switchgrass bales. AIMS Bioengineering, 2016, 3(2): 125-138. doi: 10.3934/bioeng.2016.2.125

References

  • 1. English BC, De La Torre Ugarte DG, Walsh ME, et al. (2006) Economic competitiveness of bioenergy production and effects on agriculture of the southern region. Agric Appl Econ 38: 389–402.
  • 2. Wright L, Turhollow A. (2010) Switchgrass selection as a “Model” bioenergy crop: a history of the process. Biomass Bioenerg 34: 851–868.    
  • 3. Lam PS, Sokhansanj S, Bi X, et al. (2008) Bulk density of wet and dry wheat straw and switchgrass particles. Appl Eng Agr 24: 351–358.    
  • 4. Mooney DF, Larson JA, English BC, et al. (2012) Effect of dry matter loss on profitability of outdoor storage of switchgrass. Biomass Bioenerg 44: 33–41.    
  • 5. Wiselogel AE, Aglevor FA, Johnson DK, et al. (1996) Compositional changes during storage of large round switchgrass bales. Bioresource Technol 56: 103–109.    
  • 6. Tao G, Lestander TA, Geladi P, et al. (2012) Biomass properties in association with plant species and assortments I: A synthesis based on literature data of energy properties. Renew Sust Energ Rev 16: 3481–3506.    
  • 7. Adler PR, Sanderson MA, Boatang AA, et al. (2009) Biomass yield and biofuel quality of switchgrass harvested in fall or spring. Agron J 98: 1518–25.
  • 8. Sanderson MA, Egg RP, Wiselogel AE. (1997) Biomass losses during harvest and storage of switchgrass. Biomass Bioenerg 12: 107–14.    
  • 9. Cundiff JS, Grisso RD. (2008) Containerized handling to minimize hauling cost of herbaceous biomass. Biomass Bioenerg 32: 308–13.    
  • 10. Monti A, Fazio S, Venturi G. (2009) The discrepancy between plot and field yields: harvest and storage losses of switchgrass. Biomass Bioenerg 33: 841–847.    
  • 11. Emery IR, Mosier NS. (2012) The impact of dry matter loss during herbaceous biomass storage on net greenhouse gas emissions from biofuels production. Biomass Bioenerg 39: 237–246.    
  • 12. Shinners KJ, Boettcher GC, Muck RE, et al. (2010) Harvest and storage of two perennial grasses as biomass feedstocks. T ASABE 53: 359–370.    
  • 13. Khanchi A, Jones CL, Sharma B, et al. (2013) Characteristics and compositional changes in round and square bales stored in south central Oklahoma. Biomass Bioenerg 58: 117–127.    
  • 14. Yu TE, English BC, Larson JA, et al. (2015) Influence of particle size and packaging on storage dry matter losses for switchgrass. Biomass Bioenerg 73: 135–144.    
  • 15. Rentizelas AA, Tolos AJ, Tatsiopoulos IP. (2009) Logistics issues of biomass: the storage problem and the multi-biomass supply chain. Renew Sustain Energ Rev 13: 887–894.    
  • 16. Kaliyan N, Schmidt DR, Morey RV, et al. (2012) 2012 Commercial-scale tub grinding of corn stover and perennial grasses. Appl Eng Agric 28: 79–85.    
  • 17. Carolan JE, Joshi S, Dale BE (2007) Technical and financial feasibility analysis of distributed bioprocessing using regional biomass pre-processing centers. J Agr Food Indust Organ 5: Article 10.
  • 18. Larson JA, Yu TH, English BC, et al. (2010) Cost evaluation of alternative switchgrass producing, harvesting, storing, and transporting systems and their logistics in the southeastern USA. Agric Fin Rev 70: 184–200.    
  • 19. Kline LM, Boyer CN, Yu TE, English BC, Larson JA, and Labbé N. (2016) Investigating the impact of biomass quality on near-infrared models for switchgrass feedstock. AIMS Bioengineering 3(1):23–43.
  • 20. Hess J R, Wright CT, Kenney KL. (2007) Cellulosic biomass feedstocks and logistics for ethanol production. Biofuels Bioprod Bioref 1: 181–190.    
  • 21. National Renewable Energy Laboratory (NREL). (2008) Determination of extractives in biomass. National Renewable Energy Lab. NREL/TP-510-42619.
  • 22. National Renewable Energy Laboratory (NREL). (2008) Determination of total solids in biomass and total dissolved solids in liquid process samples. National Renewable Energy Lab. NREL/TP-510-42621.
  • 23. National Renewable Energy Laboratory (NREL). (2008) Determination of structural carbohydrates and lignin in biomass, National Renewable Energy Lab. NREL/TP-510-42618.
  • 24. Esbensen KH. (1994) Multivariate data analysis in practice: An introduction to multivariate data analysis and experimental design, 5th edition. Oslo: CAMO Process AS.
  • 25. Rials TG, Kelley SS, So CL. (2002) Use of advanced spectroscopic techniques for predicting the mechanical properties of wood composites. Wood Fiber Sci 34: 398–407.
  • 26. Kuehl RO (2000) Design of experiments: statistical principles of research design and analysis. 2nd ed. Belmont: Cengage Learning.
  • 27. SAS Institute Inc. (2003) SAS system under Microsoft Windows. Release 9.2. Cary, NC.
  • 28. Greene WH. Econometric Analysis, sixth ed. Upper Saddle River, NJ; 2008.

 

This article has been cited by

  • 1. Mario Aboytes-Ojeda, Krystel Castillo-Villar, Tun-hsiang Yu, Christopher Boyer, Burton English, James Larson, Lindsey Kline, Nicole Labbé, A Principal Component Analysis in Switchgrass Chemical Composition, Energies, 2016, 9, 11, 913, 10.3390/en9110913

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Copyright Info: 2016, Christopher N. Boyer, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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