Degree Type

Thesis

Date of Award

2014

Degree Name

Master of Science

Department

Agricultural and Biosystems Engineering

First Advisor

Matthew J. Darr

Abstract

As cellulosic ethanol production reaches commercial scale, it is important to maximize efficiencies throughout the supply chain in order to keep an economically feasible feedstock. One important sub-process is the harvesting of feedstock that will be converted into ethanol. The cost to harvest and transport corn stover is a large component of the total cost and is estimated at $82/std. ton; however, this can be reduced to $47/std. ton with improvements to the supply chain (Shah, 2013).

For a large scale facility, capable of producing 30 million gallons of ethanol, 375,000 tons of dry material per year will be required to keep the facility running at full capacity; this material will need to be harvested from approximately 190,000 acres, based on a two ton per acre take-rate. The main harvest method is a multi-pass system that requires several agriculture machines working in synchronization to produce a dense and transportable material. Over 200 tractors coupled to shredders, balers and stackers are required in order to achieve the full harvest within 30 day window.

The objective of this research was to automate the analysis of Geographical Information Systems (GIS) data in order to provide more adequate real time performance of crews and machines that will drive key supply chain assessments. Results of this work analyzed production scale harvest data during the fall of 2012 and 2013; during 2012 6,000 hectares were harvested while 24,300 hectares were harvested in 2013. The results of this research will benefit cellulosic harvesters, processors and analyzers by providing informative supply chain logistics.

DOI

https://doi.org/10.31274/etd-180810-1337

Copyright Owner

Jeff C. Askey

Language

en

File Format

application/pdf

File Size

77 pages

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