Optimal Design of the Biofuel Supply Chain Utilizing Multiple Feedstocks: A Korean Case Study

Abstract

As the global climate crisis worsens and fossil fuels deplete, renewable energy sources have become more appealing. Biofuel is one of the promising sources produced from multiple biomass sources, as long as it does not compromise food security. In this direction, a multiperiod mixed-integer linear programming model incorporating three types of biomass simultaneously is developed to provide a second- and third-generation biofuel supply chain. The proposed model minimizes the total annualized cost by selecting raw materials, locating production facilities, placing storage facilities, and identifying optimal material flows by defining a deference path for each feedstock. A case study addressing bioethanol production in South Korea was then conducted to evaluate the performance of the developed model; the results demonstrated a clear reduction in the total annualized cost by incorporating multiple feedstocks. Using multiple feedstocks allowed for the total ethanol demand of South Korea to be met using harvested biomass, thereby drastically reducing import costs. Refinery costs accounted for the large majority of the remaining costs. A sensitivity analysis was also conducted to assess the effect of uncertainty associated with some economic and production parameters.

Optimal design of a biofuel supply chain using an augmented multi-objective and TOPSIS method

“…Optimization of the multiple-feedstock logistics was formulated as a MILP model, as performed in a previous study. 22 However, in this study, another objective is added to the biofuel supply chain model and changed to a multi-objective objective combining the same biomass sources. A second objective function has to do with the environmental performance of the supply chain in terms of minimizing GHG emissions.…”

“…The capacity and quantity of the dryer are calculated in eqn (20) and (21), respectively. Eqn (22) shows the mass balance for biomass storage; that is, the total storage inflows plus storage level equals the amount outflowed plus the amount inventoried in the next time period. Eqn (23) determines feedstock deterioration in the storage during the time period.…”

“…Eqn (28) states the distribution of biofuel from the refinery to demand and biofuel storage. Eqn (29) imposes the mass balance for biofuel storage, similar to eqn (22). The inventory…”

A Robust Possibilistic Bi-Objective Mixed Integer Model for Green Biofuel Supply Chain Design under Uncertain Conditions

“…Meanwhile, Ghadimi et al [28] manipulated a new multi-echelon MSW-to-biofuel supply chain network design based on a sustainable cross-efficiency DEA model to find the most suitable location of facilities. Zarei et al [29] presented a multi-period, multi-feedstocks MIP model to design second-and third-generation BSCN to minimize total costs. Nur et al [30] manipulated a two-stage stochastic MIP model by considering the biomass quality parameters for the optimal design of the biofuel supply chain.…”

Life Cycle Assessment of Inland Green Hydrogen Supply Chain Networks with Current Challenges and Future Prospects

“…In the current era of the escalating climate change and global energy demand, strict environmental regulations and the urgent need for renewable energy systems are imperative to sustainably mitigate the detrimental effects of fossil fuels on the environment. , A report by the US Department of Energy estimates that by 2050, wind and solar energy will entirely replace fossil fuels in supporting technologies, such as electric vehicles and green hydrogen production . Despite such a promising potential, the electricity production from PV systems and windmills and its efficiency are major bottlenecks that significantly depend on the availability of solar irradiance profiles and wind speed, which considerably vary based on the geographical location. − Considering this, electricity conversion plants for solar PV and windmills must be installed in the regions where the solar irradiance profiles and wind speed are optimal.…”