A MILP framework for optimal biorefinery design that accounts for forest biomass dynamics

Theozzo, Bruno; Santos, Moisés Teles dos

doi:10.1016/j.compchemeng.2020.107201

Cited by 13 publications

(1 citation statement)

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“…The supply of biomass raw material, biofuel production and demand, price, and logistics are the standard parameters associated with the uncertainty. The study of MILP models allows us to observe many studies related to the optimization models for the BSC network design under uncertainty [15]. Dalmas et al [16], for example, designed a random dynamic MILP model for designing a BSC under market uncertainty conditions.…”

Section: Introductionmentioning

confidence: 99%

The Biomass Supply Chain Network Auto-Regressive Moving Average Algorithm

2021

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Renewable power plants contribute almost insignificantly in the country's electricity supply. Due to the environmental uncertainty and the rapid development of modern technology in the recent century, it is usually necessary to predict future situations using insufficient data over a short period. Renewable fuels and biofuels as a significant replacement for fossil fuels have been of great interest in recent decades. Optimum design of supply chains is an essential requirement for the commercial production of biofuels. This research has employed mathematical optimization and a mixed integrated linear programming (MILP) approach for the feedstock pathway to the fuel supply chain (BSC) scenario. The further examination includes the effect of the autoregressive moving average (ARMA) time series structure for the demand of biofuels on the design of the supply chain. After developing the model, a numerical example of 8 years was used in the supply chain to gain a wider perspective. The results showed that the optimal cost can be determined and 4 valid locations were adopted out of 10 proposed refinery building sites. Finally, it evaluates and validates the proposed model with experimental results.

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Section: Introductionmentioning

confidence: 99%