282840 Developing Distributions for Biomass Feedstock Supplies

Monday, October 29, 2012
Hall B (Convention Center )
Joseph Amundson, University of Kentucky, Lexington, KY, Erdinc Oner, Department of Industrial Systems Engineering, Izmir University of Economics, Izmır, Turkey, Jeffrey Seay, Chemical and Materials Engineering, University of Kentucky and Fazleena Badurdeen, Institute for Sustainable Manufacturing, Lexington, KY; Mechanical Engineering, University of Kentucky, Lexington, KY

Biomass as an alternative for fossil fuels as a resource to create fuels for transportation intrigues many researchers largely due to its widespread availability and applicability as a potentially sustainable resource. Toward this end, progress has been made in recent years with regards to modeling integrated biorefineries and the supply chains necessary to provide them with raw materials. Decision support tools will provide potential investors, agriculture workers, policy makers, and other stakeholders with a means to judge the suitability and sustainability of nascent industries in specific regions because of this research in optimization and simulation.

Not least among the challenges of biomass as an alternative to fossil fuels is the seasonal and highly variable supply that is inherent to agricultural products. Due to a relatively low energy density of biomass feedstocks, large quantities of material may be required for conversion. Reasonable estimations of regional biomass that incorporate the stochastic nature of the supply become key to having any type of meaningful decision support tool.

This presentation focuses on the development of distributions for corn stover availability in the Jackson Purchase Region of Western Kentucky. At the current resolution (mostly monthly or yearly) the supply information cannot be used directly in any models, particularly those looking to simulate performance of corn stover based bioenergy systems. However, combinations of the available historical data have been used to develop distributions for each county in the region and for each month during a typical corn-harvesting season in Western Kentucky. Arena Input Analyzer and Minitab were used to develop the distributions and comparisons are drawn. These distributions provide the resolution necessary to begin region specific modeling.

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