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A detailed MILP formulation for the optimal design of advanced biofuel supply chains

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  • Moretti, Luca
  • Milani, Mario
  • Lozza, Giovanni Gustavo
  • Manzolini, Giampaolo

Abstract

The optimal design of a biomass supply chain is a complex problem, which must take into account multiple interrelated factors (i.e the spatial distribution of the network nodes, the efficient planning of logistics activities, etc.). Mixed Integer Linear Programming has proven to be an effective mathematical tool for the optimization of the design and the management strategy of Advanced Biofuel Supply Chains (ABSC). This work presents a MILP formulation of the economical optimization of ABSC design, comprising the definition of the associated weekly management plan. A general modeling approach is proposed with a network structure comprising two intermediate echelons (storage and conversion facilities) and accounts for train and truck freight transport. The model is declined for the case of a multi-feedstock ABSC for green methanol production tested on the Italian case study. Residual biomass feedstocks considered are woodchips from primary forestry residues, grape pomace, and exhausted olive pomace. The calculated cost of methanol is equal to 418.7 €/t with conversion facility cost accounting for 50% of the fuel cost share while transportation and storage costs for around 15%. When considering only woodchips the price of methanol increases to 433.4 €/t outlining the advantages of multi-feedstock approach.

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  • Moretti, Luca & Milani, Mario & Lozza, Giovanni Gustavo & Manzolini, Giampaolo, 2021. "A detailed MILP formulation for the optimal design of advanced biofuel supply chains," Renewable Energy, Elsevier, vol. 171(C), pages 159-175.
    • Handle: RePEc:eee:renene:v:171:y:2021:i:c:p:159-175
    DOI: 10.1016/j.renene.2021.02.043
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    References listed on IDEAS

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    6. Guo, Changqiang & Hu, Hao & Wang, Shaowen & Rodriguez, Luis F. & Ting, K.C. & Lin, Tao, 2022. "Multiperiod stochastic programming for biomass supply chain design under spatiotemporal variability of feedstock supply," Renewable Energy, Elsevier, vol. 186(C), pages 378-393.
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