Plant capacity level and location as a mechanism for sustainability in biomass supply chain

Abstract

Biomass is an important energy source that has the ability to reduce dependencies on fossil fuels, while providing a greener source of energy and helping achieve sustainability. Among the most commonly used biomass feedstock is corn stover, corn residue remaining in the fields after harvesting. One of the biggest challenges of using corn stover as biomass feedstock is that burning it in field is the fastest and cheapest way for many growers so as to remove it and grow new crops. This leftover corn stover could be, instead, converted to bioethanol. In this work, we propose a decision support system for expanding existing biorefineries or building new ones to help stakeholders design a supply chain network model that converts all of the available corn stover to bioethanol. Two configurations presented in this study which is the existing plant expansion (EP) configuration and the combination of existing and new plant configuration (ENP), by exploring the incentive and greenhouse gas (GHG) emission price value for the bioenergy plant to achieve the goal. The aim of converting all corn stover is successfully achieved along with the other goals of achieving sustainability by reducing the amount of GHG emissions in the supply chain. Our results reveal that we can achieve a minimum amount of GHG emissions, while maximizing profit from the supply chain, when expanding existing plants and building new plants (ENP configuration) leading to a reduction of GHG emissions by 4%.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19

References

  1. 1.

    Ghatak, H.R.: Biorefineries from the perspective of sustainability: feedstocks, products, and processes. Renew. Sustain. Energy Rev. 15, 4042–4052 (2011). https://doi.org/10.1016/j.rser.2011.07.034

    Article  Google Scholar 

  2. 2.

    U.S. Energy Information Administration.: Biomass—energy explained, your guide to understanding energy. Energy Information Administration (2017). https://www.eia.gov/energyexplained/?page=biomass_home#tab1

  3. 3.

    Perlack, R.D., Wright, L.L., Turhollow, A.F., Graham, R.L., Stokes, B.J., Erbach, D.C.: Biomass as feedstock for a bioenergy and bioproducts industry: the technical feasibility of a billion-ton annual supply (2005). https://www1.eere.energy.gov/bioenergy/pdfs/final_billionton_vision_report2.pdf

  4. 4.

    Sharma, B., Ingalls, R.G., Jones, C.L., Khanchi, A.: Biomass supply chain design and analysis: basis, overview, modeling, challenges, and future. Renew. Sustain. Energy Rev. 24, 608–627 (2013). https://doi.org/10.1016/j.rser.2013.03.049

    Article  Google Scholar 

  5. 5.

    Young, J.D., Anderson, N.M., Naughton, H.T., Mullan, K.: Economic and policy factors driving adoption of institutional woody biomass heating systems in the U.S. Energy Econ. 69, 456–470 (2018). https://doi.org/10.1016/j.eneco.2017.11.020

    Article  Google Scholar 

  6. 6.

    Mohamed Abdul Ghani, N.M.A., Egilmez, G., Kucukvar, M., S. Bhutta, M.K.: From green buildings to green supply chains. Manag. Environ. Qual. 28, 532–548 (2017). https://doi.org/10.1108/meq-12-2015-0211

    Article  Google Scholar 

  7. 7.

    Shafiee, S., Topal, E.: When will fossil fuel reserves be diminished? Energy Policy 37, 181–189 (2009). https://doi.org/10.1016/j.enpol.2008.08.016

    Article  Google Scholar 

  8. 8.

    Raftery, J.P., Karim, M.N.: Economic viability of consolidated bioprocessing utilizing multiple biomass substrates for commercial-scale cellulosic bioethanol production. Biomass Bioenergy 103, 35–46 (2017). https://doi.org/10.1016/j.biombioe.2017.05.012

    Article  Google Scholar 

  9. 9.

    Lainez-Aguirre, J.M., Pérez-Fortes, M., Puigjaner, L.: Economic evaluation of bio-based supply chains with CO2 capture and utilisation. Comput. Chem. Eng. 102, 213–225 (2017). https://doi.org/10.1016/j.compchemeng.2016.09.007

    Article  Google Scholar 

  10. 10.

    Akgul, O., Shah, N., Papageorgiou, L.G.: Economic optimisation of a UK advanced biofuel supply chain. Biomass Bioenergy 41, 57–72 (2012). https://doi.org/10.1016/j.biombioe.2012.01.040

    Article  Google Scholar 

  11. 11.

    Kim, S., Dale, B.E.: Comparing alternative cellulosic biomass biorefining systems: centralized versus distributed processing systems. Biomass Bioenergy 74, 135–147 (2015). https://doi.org/10.1016/j.biombioe.2015.01.018

    Article  Google Scholar 

  12. 12.

    Wang, Y., Ebadian, M., Sokhansanj, S., Webb, E., Lau, A.: Impact of the biorefinery size on the logistics of corn stover supply—a scenario analysis. Appl. Energy 198, 360–376 (2017). https://doi.org/10.1016/j.apenergy.2017.03.056

    Article  Google Scholar 

  13. 13.

    Clauser, N.M., Gutiérrez, S., Area, M.C., Felissia, F.E., Vallejos, M.E.: Small-sized biorefineries as strategy to add value to sugarcane bagasse. Chem. Eng. Res. Des. 107, 137–146 (2016). https://doi.org/10.1016/j.cherd.2015.10.050

    Article  Google Scholar 

  14. 14.

    Gonzales, D.S., Searcy, S.W.: GIS-based allocation of herbaceous biomass in biorefineries and depots. Biomass Bioenergy 97, 1–10 (2017). https://doi.org/10.1016/j.biombioe.2016.12.009

    Article  Google Scholar 

  15. 15.

    Sahoo, K., Hawkins, G.L., Yao, X.A., Samples, K., Mani, S.: GIS-based biomass assessment and supply logistics system for a sustainable biorefinery: a case study with cotton stalks in the Southeastern US. Appl. Energy 182, 260–273 (2016). https://doi.org/10.1016/j.apenergy.2016.08.114

    Article  Google Scholar 

  16. 16.

    Zhang, F., Wang, J., Liu, S., Zhang, S., Sutherland, J.W.: Integrating GIS with optimization method for a biofuel feedstock supply chain. Biomass Bioenergy 98, 194–205 (2017). https://doi.org/10.1016/j.biombioe.2017.01.004

    Article  Google Scholar 

  17. 17.

    Craige, C.C., Buser, M.D., Frazier, R.S., Hiziroglu, S.S., Holcomb, R.B., Huhnke, R.L.: Conceptual design of a biofeedstock supply chain model for eastern redcedar. Comput. Electron. Agric. 121, 12–24 (2016). https://doi.org/10.1016/j.compag.2015.11.019

    Article  Google Scholar 

  18. 18.

    De Meyer, A., Cattrysse, D., Van Orshoven, J.: Considering biomass growth and regeneration in the optimisation of biomass supply chains. Renew. Energy 87, 990–1002 (2016). https://doi.org/10.1016/j.renene.2015.07.043

    Article  Google Scholar 

  19. 19.

    Bai, Y., Hwang, T., Kang, S., Ouyang, Y.: Biofuel refinery location and supply chain planning under traffic congestion. Transp. Res. Part B Methodol. 45, 162–175 (2011). https://doi.org/10.1016/j.trb.2010.04.006

    Article  Google Scholar 

  20. 20.

    Parker, N., Tittmann, P., Hart, Q., Nelson, R., Skog, K., Schmidt, A., Gray, E., Jenkins, B.: Development of a biorefinery optimized biofuel supply curve for the Western United States. Biomass Bioenergy 34, 1597–1607 (2010). https://doi.org/10.1016/j.biombioe.2010.06.007

    Article  Google Scholar 

  21. 21.

    Li, Y., Hu, G., Wright, M.M.: An optimization model for sequential fast pyrolysis facility location-allocation under renewable fuel standard. Energy 93, 1165–1172 (2015). https://doi.org/10.1016/j.energy.2015.09.090

    Article  Google Scholar 

  22. 22.

    Huang, Y., Chen, Y.: Analysis of an imperfectly competitive cellulosic biofuel supply chain. Transp. Res. Part E Logist. Transp. Rev. 72, 1–14 (2014). https://doi.org/10.1016/j.tre.2014.09.008

    Article  Google Scholar 

  23. 23.

    Ng, R.T.L., Maravelias, C.T.: Economic and energetic analysis of biofuel supply chains. Appl. Energy 205, 1571–1582 (2017). https://doi.org/10.1016/j.apenergy.2017.08.161

    Article  Google Scholar 

  24. 24.

    Hu, C., Liu, X., Lu, J.: A bi-objective two-stage robust location model for waste-to-energy facilities under uncertainty. Decis. Support Syst. 99, 37–50 (2017). https://doi.org/10.1016/j.dss.2017.05.009

    Article  Google Scholar 

  25. 25.

    Lauven, L.P.: An optimization approach to biorefinery setup planning. Biomass Bioenergy 70, 440–451 (2014). https://doi.org/10.1016/j.biombioe.2014.07.026

    Article  Google Scholar 

  26. 26.

    Ekşioğlu, S.D., Acharya, A., Leightley, L.E., Arora, S.: Analyzing the design and management of biomass-to-biorefinery supply chain. Comput. Ind. Eng. 57, 1342–1352 (2009). https://doi.org/10.1016/j.cie.2009.07.003

    Article  Google Scholar 

  27. 27.

    Rentizelas, A.A., Tatsiopoulos, I.P.: Locating a bioenergy facility using a hybrid optimization method. Int. J. Prod. Econ. 123, 196–209 (2010). https://doi.org/10.1016/j.ijpe.2009.08.013

    Article  Google Scholar 

  28. 28.

    Ghafghazi, S., Sowlati, T., Sokhansanj, S., Bi, X., Melin, S.: Life cycle assessment of base–load heat sources for district heating system options. Int. J. Life Cycle Assess. 16, 212–223 (2011). https://doi.org/10.1007/s11367-011-0259-9

    Article  Google Scholar 

  29. 29.

    Ebadian, M., Sowlati, T., Sokhansanj, S., Townley-Smith, L., Stumborg, M.: Modeling and analysing storage systems in agricultural biomass supply chain for cellulosic ethanol production. Appl. Energy 102, 840–849 (2013). https://doi.org/10.1016/j.apenergy.2012.08.049

    Article  Google Scholar 

  30. 30.

    Chang, K.H.: A decision support system for planning and coordination of hybrid renewable energy systems. Decis. Support Syst. 64, 4–13 (2014). https://doi.org/10.1016/j.dss.2014.04.001

    Article  Google Scholar 

  31. 31.

    Mattiussi, A., Rosano, M., Simeoni, P.: A decision support system for sustainable energy supply combining multi-objective and multi-attribute analysis: an Australian case study. Decis. Support Syst. 57, 150–159 (2014). https://doi.org/10.1016/j.dss.2013.08.013

    Article  Google Scholar 

  32. 32.

    Hunt, J.D., Bañares-Alcántara, R., Hanbury, D.: A new integrated tool for complex decision making: application to the UK energy sector. Decis. Support Syst. 54, 1427–1441 (2013). https://doi.org/10.1016/j.dss.2012.12.010

    Article  Google Scholar 

  33. 33.

    Kurkalova, L.A., Carter, L.: Sustainable production: using simulation modeling to identify the benefits of green information systems. Decis. Support Syst. 96, 83–91 (2017). https://doi.org/10.1016/j.dss.2017.02.006

    Article  Google Scholar 

  34. 34.

    Hakanen, J., Miettinen, K., Sahlstedt, K.: Wastewater treatment: new insight provided by interactive multiobjective optimization. Decis. Support Syst. 51, 328–337 (2011). https://doi.org/10.1016/j.dss.2010.11.026

    Article  Google Scholar 

  35. 35.

    Macharis, C., Turcksin, L., Lebeau, K.: Multi actor multi criteria analysis (MAMCA) as a tool to support sustainable decisions: state of use. Decis. Support Syst. 54, 610–620 (2012). https://doi.org/10.1016/j.dss.2012.08.008

    Article  Google Scholar 

  36. 36.

    Rosaly, B., Laurèn, D.: Renewable Energy 101 and the Importance of Incentives_The Sustainability Co-Op (2013). https://thesustainabilitycooperative.net/2013/10/13/all-about-renewables/

  37. 37.

    Bangalore, M., Hochman, G., Zilberman, D.: Policy incentives and adoption of agricultural anaerobic digestion: a survey of Europe and the United States. Renew. Energy 97, 559–571 (2016). https://doi.org/10.1016/j.renene.2016.05.062

    Article  Google Scholar 

  38. 38.

    Simsek, H.A., Simsek, N.: Recent incentives for renewable energy in turkey. Energy Policy 63, 521–530 (2013). https://doi.org/10.1016/j.enpol.2013.08.036

    Article  Google Scholar 

  39. 39.

    Tongsopit, S., Greacen, C.: An assessment of Thailand’s feed-in tariff program. Renew. Energy 60, 439–445 (2013). https://doi.org/10.1016/j.renene.2013.05.036

    Article  Google Scholar 

  40. 40.

    Supriyasilp, T., Pinitjitsamut, M., Pongput, K., Wanaset, A., Boonyanupong, S., Rakthai, S., Boonyasirikul, T.: A challenge of incentive for small hydropower commercial investment in Thailand. Renew. Energy 111, 861–869 (2017). https://doi.org/10.1016/j.renene.2017.05.009

    Article  Google Scholar 

  41. 41.

    Ozcan, M.: Assessment of renewable energy incentive system from investors’ perspective. Renew. Energy 71, 425–432 (2014). https://doi.org/10.1016/j.renene.2014.05.053

    Article  Google Scholar 

  42. 42.

    Cobuloglu, H.I., Büyüktahtakın, İ.E.: A mixed-integer optimization model for the economic and environmental analysis of biomass production. Biomass Bioenergy 67, 8–23 (2014). https://doi.org/10.1016/j.biombioe.2014.03.025

    Article  Google Scholar 

  43. 43.

    Fattahi, M., Govindan, K.: A multi-stage stochastic program for the sustainable design of biofuel supply chain networks under biomass supply uncertainty and disruption risk: a real-life case study. Transp. Res. Part E Logist. Transp. Rev. (2018). https://doi.org/10.1016/j.tre.2018.08.008

    Article  Google Scholar 

  44. 44.

    Asadi, E., Habibi, F., Nickel, S., Sahebi, H.: A bi-objective stochastic location-inventory-routing model for microalgae-based biofuel supply chain. Appl. Energy (2018). https://doi.org/10.1016/j.apenergy.2018.07.067

    Article  Google Scholar 

  45. 45.

    Xie, F., Huang, Y.: A multistage stochastic programming model for a multi-period strategic expansion of biofuel supply chain under evolving uncertainties. Transp. Res. Part E Logist. Transp. Rev. (2018). https://doi.org/10.1016/j.tre.2018.01.015

    Article  Google Scholar 

  46. 46.

    Liu, Z., Johnson, T.G., Altman, I.: The moderating role of biomass availability in biopower co-firing—a sensitivity analysis. J. Clean. Prod. 135, 523–532 (2016). https://doi.org/10.1016/j.jclepro.2016.06.101

    Article  Google Scholar 

  47. 47.

    Mohamed Abdul Ghani, N.M.A., Vogiatzis, C., Szmerekovsky, J.: Biomass feedstock supply chain network design with biomass conversion incentives. Energy Policy 116, 39–49 (2018). https://doi.org/10.1016/j.enpol.2018.01.042

    Article  Google Scholar 

  48. 48.

    Henke, J., ISCC.: Low Carbon Fuel Regulation in North America and the EU (2018). https://www.iscc-system.org/wp-content/uploads/2017/02/4.-Henke_Low-Carbon-Fuel-Regulation-in-North-America-and-the-EU_ISCCConference-Bogota%CC%81-2018.pdf

  49. 49.

    USDA.: Corn 57 (2016). https://www.usda.gov/nass/PUBS/TODAYRPT/acrg0616.pdf

  50. 50.

    Mayer, M.: Placing a Value on Corn Stover. UW Ext. 2012 (2012). https://green.extension.wisc.edu/files/2010/05/Placing-a-Value-on-Corn-Stover.pdf

  51. 51.

    Maung, T.A., Gustafson, C.R.: The viability of harvesting corn cobs and stover for biofuel production in North Dakota (2011). https://ideas.repec.org/p/ags/aaea11/103613.html

  52. 52.

    Tao, L., Templeton, D.W., Humbird, D., Aden, A.: Bioresource technology effect of corn stover compositional variability on minimum ethanol selling price (MESP). Bioresour. Technol. 140, 426–430 (2013). https://doi.org/10.1016/j.biortech.2013.04.083

    Article  Google Scholar 

  53. 53.

    Gallagher, P.W.: Biomass supply from corn residues: estimates and critical review of procedures (2012). https://www.usda.gov/oce/reports/energy/Biomass%20Supply%20From%20Corn%20Residues-Nov-2012.pdf

  54. 54.

    Gustafson, C.R., Maung, T.A., Saxowsky, D.: Economics of sourcing cellulosic feedstock for energy production (2011). https://ideas.repec.org/p/ags/aaea11/103260.html

  55. 55.

    Xie, F., Huang, Y., Eksioglu, S.: Bioresource technology integrating multimodal transport into cellulosic biofuel supply chain design under feedstock seasonality with a case study based on California. Bioresour. Technol. 152, 15–23 (2014). https://doi.org/10.1016/j.biortech.2013.10.074

    Article  Google Scholar 

  56. 56.

    Humbird, D., Davis, R., Tao, L., Kinchin, C., Hsu, D., Aden, A., Schoen, P., Lukas, J., Olthof, B., Worley, M., Sexton, D.: Process design and economics for biochemical conversion of lignocellulosic biomass to ethanol (2011). https://www.osti.gov/biblio/1013269-process-design-economics-biochemicalconversion-lignocellulosic-biomass-ethanol-dilute-acid-pretreatment-enzymatichydrolysis-corn-stover

  57. 57.

    Aakre, D.G., Haugen, R.: Results of the North Dakota land valuation model for the 2013 agricultural real estate assessment, p. 21 (2013). https://ideas.repec.org/p/ags/nddaae/157657.html

  58. 58.

    Mcaloon, A., Taylor, F., Yee, W., Regional, E., Ibsen, K., Wooley, R., Biotechnology, N.: Determining the cost of producing ethanol from corn starch and lignocellulosic feedstocks (2000). https://www.nrel.gov/docs/fy01osti/28893.pdf

  59. 59.

    ESRI.com.: ESRI (2016). https://www.esri.com/en-us/home

  60. 60.

    Osmani, A., Zhang, J.: Multi-period stochastic optimization of a sustainable multi-feedstock second generation bioethanol supply chain—a logistic case study in Midwestern United States. Land Use Policy 61, 420–450 (2017). https://doi.org/10.1016/j.landusepol.2016.10.028

    Article  Google Scholar 

  61. 61.

    OpenSolver.: OpenSolver for Excel—The Open Source Optimization Solver for Excel. https://opensolver.org/

  62. 62.

    Brechbill, S., Tyner, W.E.: The economics of renewable energy: corn stover and switchgrass. Perdue Ext. Publ., pp 1–6. (2008). https://www.extension.purdue.edu/extmedia/ID/ID-404.pdf

  63. 63.

    James, D.S.: The viability of lignocellulosic ethanol production as a business endeavour in Canada (2013). https://open.library.ubc.ca/cIRcle/collections/ubctheses/24/items/1.0073518

Download references

Acknowledgements

Part of this work was performed while Dr. Chrysafis Vogiatzis was an Assistant Professor with the Department of Industrial and Manufacturing Engineering at North Dakota State University. Funding: Chrysafis Vogiatzis would like to acknowledge support by Grant ND EPSCoR NSF 1355466.

Author information

Affiliations

Authors

Corresponding author

Correspondence to N. Muhammad Aslaam Mohamed Abdul Ghani.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mohamed Abdul Ghani, N.M.A., Szmerekovsky, J.G. & Vogiatzis, C. Plant capacity level and location as a mechanism for sustainability in biomass supply chain. Energy Syst 11, 1075–1109 (2020). https://doi.org/10.1007/s12667-019-00361-z

Download citation

Keywords

  • Biomass
  • Sustainable supply chain management
  • Incentives
  • Optimization
  • Facility location