A mixed uncertainty approach to design a bioenergy network considering sustainability and efficiency measures. (June 2021)

• Record Type: Journal Article
• Authors: Samani, Mohammad Reza Ghatreh; Hosseini-Motlagh, Seyyed-Mahdi
• Is Part Of: Computers & chemical engineering. Volume 149(2021)
• Publication Date: 2021-06
• Subjects: Bioenergy network -- Azadirachta indica -- Eruca sativa -- Sustainability -- Efficiency -- Uncertainty; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2021.107305
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: Designing a BSCN by considering two high potential energy crops: ES and AI. Generating bioelectricity using wastewater discharged from industrial facilities. Decreasing the C O 2 emissions and increasing the efficiency by applying a DEA. Applying an MRPP approach to deal with epistemic uncertainty. Using a real case to assess the practicality of the BSCN. Abstract: Environmental concerns such as water pollution and global warming resulted in considerable attention being paid to sustainable bioenergy. Due to the eco-friendly nature of sustainable bioenergy, we investigated an integrated bioenergy supply chain network design. In the proposed network, different energy crops, Azadirachta indica and Eruca sativa are taken into account, which leads to different sources for bioenergy production. Furthermore, to prevent further environmental pollution, the wastewater produced at biorefinery is used to generate bioelectricity. This paper addresses a sustainable-efficient bioenergy supply chain network design under uncertainty. A multi-period, multi-product mathematical model is proposed to minimize inefficiency, environmental impacts, and supply chain costs. Furthermore, to cope with uncertainty, a mixed robust-possibilistic programming approach is applied to the model. Finally, computational results based on Iran's real-world case demonstrate the improved performance of the designed bioenergy network by considering three measures of cost, efficiency, and environmental impacts.