A network design model for biomass to energy supply chains with anaerobic digestion systems


Balaman S. Y., SELİM H.

APPLIED ENERGY, cilt.130, ss.289-304, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 130
  • Basım Tarihi: 2014
  • Doi Numarası: 10.1016/j.apenergy.2014.05.043
  • Dergi Adı: APPLIED ENERGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.289-304
  • Anahtar Kelimeler: Biomass to energy conversion systems, Biofuel supply chain, Anaerobic digestion systems, Biogas, Mixed integer linear programming, OPTIMIZATION MODEL, LOGISTICS SYSTEM, IMPLEMENTATION, WASTE
  • Dokuz Eylül Üniversitesi Adresli: Evet

Özet

Development and implementation of renewable energy systems, as a part of the solution to the worldwide increasing energy consumption, have been considered as emerging areas to offer an alternative to the traditional energy systems with limited fossil fuel resources and to challenge environmental problems caused by them. Biomass is one of the alternative energy resources and agricultural, animal and industrial organic wastes can be treated as biomass feedstock in biomass to energy conversion systems. This study aims to develop an effective supply chain network design model for the production of biogas through anaerobic digestion of biomass. In this regard, a mixed integer linear programming model is developed to determine the most appropriate locations for the biogas plants and biomass storages. Besides the strategic decisions such as determining the numbers, capacities and locations of biogas plants and biomass storages, the biomass supply and product distribution decisions can also be made by this model. Mainly, waste biomass is considered as feedstock to be digested in anaerobic digestion facilities. To explore the viability of the proposed model, computational experiments are performed on a real-world problem. Additionally, a sensitivity analysis is performed to account for the uncertainties in the input data to the decision problem. (C) 2014 Elsevier Ltd. All rights reserved.