Modeling and economic optimization of cellulosic biofuel supply chain considering multiple conversion pathways
•A new model considering multiple conversion pathways is established.•Twelve conversion technologies are implemented in the model.•Case studies are conducted to demonstrate the model’s effectiveness.•The optimal planning strategy can save up to 14.6% of the total cost.•Biorefinery construction cost...
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Veröffentlicht in: | Applied energy 2021-01, Vol.281, p.116059, Article 116059 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | •A new model considering multiple conversion pathways is established.•Twelve conversion technologies are implemented in the model.•Case studies are conducted to demonstrate the model’s effectiveness.•The optimal planning strategy can save up to 14.6% of the total cost.•Biorefinery construction cost is the main driver for the biofuel supply chain.
Cellulosic biofuel is considered a promising alternative to traditional fossil fuels. Currently, the commercialization of cellulosic biofuel is proceeding at a slower pace than expected due to the high cost of the cellulosic biofuel supply chain (CBSC), especially in the manufacturing process. In this paper, a new model for a four-echelon CBSC incorporating 12 conversion pathways is proposed to minimize total supply chain cost by considering multiple time periods, various biomass types and spatial distributions, biomass and biofuel inventories, logistics, and different demand levels. Case studies from the state of Illinois in the U.S. are used to demonstrate the effectiveness of the proposed model. It is manifested that the model can select appropriate conversion pathways for biorefineries given the abundance of local resources to minimize the overall cost. Compared to the two cases adopted a single conversion pathway, the optimal result achieves 14.6% and 4.6% reductions on the unit biofuel cost, respectively. In addition, the results of sensitivity analysis indicate that the biorefinery construction cost and the biofuel throughput are the main economic drivers in the current supply chain design. |
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ISSN: | 0306-2619 1872-9118 |
DOI: | 10.1016/j.apenergy.2020.116059 |