Cost modeling, production modeling, continuous system, simulation, cellulosic ethanol, deterministic


The use of biofuels in the United States has increased dramatically in the last few years. The largest source of feedstock for ethanol to date has been corn. However, corn is also a vitally important food crop and is used commonly as feed for cattle and other livestock. To prevent further diversion of an important food crop to production of ethanol, there is great interest in developing commercial-scale technologies to make ethanol from non-food crops, or other suitable plant material. This is commonly referred to as biomass. A review is made of lignocellulosic sources being considered as feedstocks to produce ethanol. Current technologies for pretreatment and hydrolysis of the biomass material are examined and discussed. Production data and cost estimates are culled from the literature, and used to assist in development of mathematical models for evaluation of production ramp-up profiles, and cost estimation. These mathematical models are useful as a planning tool, and provide a methodology to estimate monthly production output and costs for labor, capital, operations and maintenance, feedstock, raw materials, and total cost. Existing credits for ethanol production are also considered and modeled. The production output in liters is modeled as a negative exponential growth curve, with a rate coefficient providing the ability to evaluate slower, or faster, growth in production output and its corresponding effect on monthly cost. The capital and labor costs per unit of product are determined by dividing the monthly debt service and labor costs by that month’s production value. The remaining cost components change at a constant rate in the simulation case studies. This methodology is used to calculate production levels and costs as a function of time for a 25 million gallon per year capacity cellulosic ethanol plant. The parameters of interest are calculated in MATLAB with a deterministic, continuous system simulation model. Simulation results for high, medium, and low cost case studies are included. Assumptions for the model and for each case study are included and some comparisons are made to cost estimates in the literature. iv While the cost per unit of product decreases and production output increases over time, some reasonable cost values are obtained by the end of the second year for both the low and medium cost case studies. By the end of Year 2, total costs for those case studies are $0.48 per liter and $0.88 per liter, respectively. These cost estimates are well within the reported range of values from the reviewed literature sources. Differing assumptions for calculations made by different sources make a direct cost comparison with the outputs of this modeling methodology extremely difficult. Proposals for reducing costs are introduced. Limitations and shortcomings of the research activity are discussed, along with recommendations for potential future work in improving the simulation model and model verification activities. In summary, the author was not able to find evidence—within the public domain—of any similar modeling and simulation methodology that uses a deterministic, continuous simulation model to evaluate production and costs as a function of time. This methodology is also unique in highlighting the important effect of production ramp-up on monthly costs for capital (debt service) and labor. The resultant simulation model can be used for planning purposes and provides an independent, unbiased estimate of cost as a function of time.


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Graduation Date





Kincaid, J. Peter


Master of Science (M.S.)


College of Engineering and Computer Science


Industrial Engineering and Management Systems

Degree Program

Modeling and Simulation








Release Date

August 2012

Length of Campus-only Access


Access Status

Masters Thesis (Open Access)


Dissertations, Academic -- Graduate Studies, Graduate Studies -- Dissertations, Academic

Included in

Engineering Commons