Modeling and analysing storage systems in agricultural biomass supply chain for cellulosic ethanol production.

Ebadian, M., Sowlati, T., Sokhansanj, S., Townley-Smith, L., and Stumborg, M. (2013). "Modeling and analysing storage systems in agricultural biomass supply chain for cellulosic ethanol production.", Applied Energy, 102(February 2013), pp. 840-849. doi : 10.1016/j.apenergy.2012.08.049  Access to full text

Abstract

In this paper, a combined simulation/optimization model is developed to better understand and evaluate the impact of the storage systems on the costs incurred by each actor in the agricultural biomass supply chain including farmers, hauling contractors and the cellulosic ethanol plant. The optimization model prescribes the optimum number and location of farms and storages. It also determines the supply radius, the number of farms required to secure the annual supply of biomass and also the assignment of farms to storage locations. Given the specific design of the supply chain determined by the optimization model, the simulation model determines the number of required machines for each operation, their daily working schedule and utilization rates, along with the capacities of storages. To evaluate the impact of the storage systems on the delivered costs, three storage systems are molded and compared: roadside storage (RS) system and two satellite storage (SS) systems including SS with fixed hauling distance (SF) and SS with variable hauling distance (SV). In all storage systems, it is assumed the loading equipment is dedicated to storage locations. The obtained results from a real case study provide detailed cost figures for each storage system since the developed model analyses the supply chain on an hourly basis and considers time-dependence and stochasticity of the supply chain. Comparison of the storage systems shows SV would outperform SF and RS by reducing the total delivered cost by 8% and 6%, respectively. However, RS results in 10% and 8% decline in consumed energy and produced CO2 in logistics operations compared to SV and SF, respectively. Another finding is that the dedication of loading equipment to storage locations is an expensive option for agricultural biomass supply chain as loading operation is utilized 3%, 6% and 11% of its annual working hours in RS, SF and SV, respectively.

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