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High-resolution multi-objective optimization of feedstock landscape design for hybrid first and second generation biorefineries

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  • Nguyen, Trung H.
  • Granger, Julien
  • Pandya, Deval
  • Paustian, Keith

Abstract

Biofuels have been proposed as a potential solution for climate change mitigation. However, there exist several barriers, such as “food vs fuel” issues and technological constraints, restricting the sustainable commercialization of both first- and second-generation biofuels. Combining arable crops and their residues for hybrid first- and second-generation biofuel production provides opportunities to overcome these barriers. This study presents a high-resolution quantitative tool to support decision-making in feedstock production and sourcing for hybrid biofuel supply chains. We demonstrate this work with a case study on optimizing feedstock landscape design for a hybrid corn grain- and stover-based ethanol production system at Front Range Energy biorefinery, Windsor, Colorado, USA using a coupled simulation modeling and life-cycle assessment approach. The case study considered three competing design objectives including the minimization of feedstock-delivered costs, farm-to-refinery greenhouse gas emissions (GHG), and nitrogen (N) leaching, subject to constraints in land use and biofuel feedstock demand. Social costs of carbon (SC-CO2) and nitrogen leaching (SC-NL) were used as weighting factors for GHG and N leaching in the objective function. Our results showed that marginal decreases of feedstock-delivered costs (below $0.31 L−1), N leaching (below 0.44 g N L−1), and GHG emissions (below 125 g CO2e L−1) resulted in extreme trade-offs among the design objectives. Changes in feedstock landscape design were most sensitive to the variations of the SC-CO2 between $400 and $800 per Mg CO2e, SC-NL between $0 and $50 per kg N leaching, and their ratio between 0 and 350, respectively.

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  • Nguyen, Trung H. & Granger, Julien & Pandya, Deval & Paustian, Keith, 2019. "High-resolution multi-objective optimization of feedstock landscape design for hybrid first and second generation biorefineries," Applied Energy, Elsevier, vol. 238(C), pages 1484-1496.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:1484-1496
    DOI: 10.1016/j.apenergy.2019.01.117
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    Cited by:

    1. Tianran Ding & Bernhard Steubing & Wouter Achten, 2022. "Coupling optimization with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359529, ULB -- Universite Libre de Bruxelles.
    2. Benedetto Rugani & Philippe Osset & Olivier Blanc & Enrico Benetto, 2023. "Environmental Footprint Neutrality Using Methods and Tools for Natural Capital Accounting in Life Cycle Assessment," Land, MDPI, vol. 12(6), pages 1-30, June.
    3. Tianran Ding & Bernhard Steubing & Wouter Achten, 2022. "Coupling optimization with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/352783, ULB -- Universite Libre de Bruxelles.
    4. Nguyen, Trung H. & Nong, Duy & Paustian, Keith, 2019. "Surrogate-based multi-objective optimization of management options for agricultural landscapes using artificial neural networks," Ecological Modelling, Elsevier, vol. 400(C), pages 1-13.
    5. Esteves, Elisa M.M. & Brigagão, George V. & Morgado, Cláudia R.V., 2021. "Multi-objective optimization of integrated crop-livestock system for biofuels production: A life-cycle approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    6. Oviroh, Peter Ozaveshe & Austin-Breneman, Jesse & Chien, Cheng-Chun & Chakravarthula, Praneet Nallan & Harikumar, Vaishnavi & Shiva, Pranjal & Kimbowa, Alvin Bagetuuma & Luntz, Jonathan & Miyingo, Emm, 2023. "Micro Water-Energy-Food (MicroWEF) Nexus: A system design optimization framework for Integrated Natural Resource Conservation and Development (INRCD) projects at community scale," Applied Energy, Elsevier, vol. 333(C).
    7. Farajiamiri, Mina & Meyer, Jörn-Christian & Walther, Grit, 2023. "Multi-objective optimization of renewable fuel supply chains regarding cost, land use, and water use," Applied Energy, Elsevier, vol. 349(C).

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