A sustainable biomass network design model for bioenergy production by anaerobic digestion technology: using agricultural residues and livestock manure

Abstract

AbstractScarcity of fossil fuels and their emissions have led energy policymakers to look for alternative renewable and clean energy sources. In line with this target, biomass is a promising alternative source for the generation of clean energy and the development of a sustainable society. The use of animal and agricultural wastes is one of the very promising renewable energy alternatives paving the way for a more sustainable energy network. Animal and agricultural wastes as biomass sources do not endanger food security and mitigate environmental impacts and may therefore considerably contribute to an appropriate waste management. As a result, converting animal and agricultural wastes to energy is a challenging issue that has attracted the attention of academic and industrial researchers. A multi-echelon multi-objective model is developed to design a sustainable supply chain for bioenergy generation through the anaerobic digestion process. The model maximizes economic and social objective functions, representing direct economic profits and positive social externalities such as job creation and economic development, respectively. Factors affecting the international supply chain include imports of intermediate production equipment, exports of a final product, international business terms applied, customs duties, and foreign exchange rates. Bioenergy and fertilizers are outputs considered in this study; the former to be converted to electricity in a biogas plant to meet domestic demands, and the latter to be exported. A case study for the Golestan province is used to evaluate the efficiency of the proposed model. The results support the potential for three biogas power plants in Gonbad-e-Kavoos, with an annual production capacity of about 1000 tons of fertilizer and an electricity supply for 101,556 households per month. There is still a broad field of promising avenues for future research. Studying uncertainty in different supply chain parameters and using robust optimization to deal with uncertainties are recommended approaches.