Using GIS and intelligent transportation tools for biomass supply chain modeling and cost assessment

Gutesa, Slobodan
Major Professor
Matthew J. Darr
Committee Member
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Agricultural and Biosystems Engineering

Stable, functional, and efficient bioethanol production systems on the national level must emphasize solutions of feedstock availability and transportation problems. Transportation logistics are a critical factor in the optimization of biomass supply chains. A single 25 million gallon per year cellulosic ethanol biorefinery will require delivery of 18,500 semi loads of bales to the plant. For a typical corn-stover biomass supply chain, baled corn stover must be transported in two phases, first from the field to a storage site and then from the storage site to the biorefinery. All activities between these two points are interconnected and together they form the biomass supply chain. The goal of supply-chain optimization is to minimize the total cost of these activities (transportation cost per unit, inventory cost per unit etc.) while satisfying the supply demands of a biorefinery.

The objective of the first chapter of this thesis is to provide a detailed report on a recent analysis of production-scale biomass transportation. Specifically, 16,000 large square bales of corn stover were harvested and hauled to satellite storage during the 2011 and 2012 harvest seasons. Intensive Geographic Information Systems (GIS) tracking and video capture of the loading, securement, hauling, and unloading events were collected and the results were summarized.

The second chapter presents specific results including: metrics for measuring supply chain efficiency, current capability of biomass supply chains, and sensitivity analysis to improvements in future supply chains. A discrete modeling technique was utilized to make proper assessment of the supply-chain system performance. The supply-chain model was a representation of a realistic biomass transportation cycle between a single cornfield and biomass storage. The discrete model included multiple simulations using different model factors. This approach provided complete assessment of influence of various factors on system productivity.

Understanding basic transportation metrics, handling parameters, and their interaction can be crucial for planning and implementing an optimal supply-chain solution.

The outcomes of this work can be used to create more efficient supply systems and to improve economic aspects of biofuel production process in general.