1

System Dynamics Modeling of Corn Ethanol as a Bio Transportation Fuel in 

the United States

Deogratias Kibira1, Guodong Shao1, and Stephen Nowak2

1

Manufacturing Systems Integration Division, National Institute of Standards and Technology, 

Gaithersburg, MD 20899, USA; 2

Franciscan University of Steubenville, Steubenville, OH 43952, USA.

Abstract

The production capacity of corn ethanol as a transportation fuel is experiencing rapid growth in 

the United States. The demand is driven by increased prices of gasoline, government mandates, 

incentives, desire for cleaner fuels, and the need to achieve energy independence. Continued 

strong growth of the corn ethanol industry will depend on profitability by both suppliers and 

producers. This in turn will be influenced by several factors such as demand, government 

incentives, feedstock availability and prices, processing plant capacity, and efficient farm and

ethanol processing technologies. How and to what extent will the projected growth of the corn 

ethanol industry in the United States be influenced by some or all of these factors? We use system 

dynamics modeling to construct the causal-loop structure of the corn ethanol industry and a stock 

and flow diagrams to explore how changes in projected factors will affect the industry. Currently, 

planners and researchers explore various energy supply options by the year 2030, when world 

energy usage is expected to double that of 2009. By developing and executing a system dynamics 

model, this paper explores different possible growth scenarios of the industry for the next twenty 

one years.

Keywords: Renewable energy; Energy independence; Corn ethanol production; System dynamics 

1. Introduction

The United States (U.S.) is facing increasing energy costs and environmental pressures calling for 

reduction in greenhouse gas emissions. A solution to escalating costs of imported oil is to promote

renewable energy sources such as ethanol. Brazil has managed to develop a fuel ethanol industry 

based on sugar cane. The U.S., on the other hand, has been primarily producing ethanol using corn 

as feedstock. Ethanol is produced by process manufacturing. But since it is a cleaner fuel its wider 

application in transportation and manufacturing would promote sustainable manufacturing, which 

is a core research topic in the Manufacturing Systems Integration Division (MSID) at the National 

Institute of Standards and Technology (NIST). This section discusses the current state of the 

ethanol industry in the U.S. and its effect on other sectors such as food prices. An overview of 

system dynamics modeling for energy analysis is presented along with the contents of the rest of 

the paper.

1.1 Background to energy needs and policy

Fossil fuels such as coal, oil, and gas are the major source of world energy, accounting for more 

than 80 % of the total energy produced [1]. Petroleum oil is the preeminent source of 

transportation energy. As of 2008 the U.S. consumes about 20 million barrels of oil per day, 60 %

of which is imported [2]. The price of oil has been escalating because of increasing demand, 
2

especially from energy intensive economies of developing countries, geopolitics, and speculation 

about shortage of supplies, among other factors. The average price of oil during the 1990s was $20 

per barrel but rose to an average of $59 during 2006. The highest price of oil exceeded $140 per 

barrel during 2008 [3]. Although the credit crunch later in the year 2008 saw a fall to $30 per 

barrel, the price of oil started going up again to more than $70 during 2009 and is expected to 

continue rising. In addition to price, the U.S. is interested in developing cleaner and renewable 

energy technologies for environmental protection, and to reduce greenhouse gases emissions. As 

an alternative to oil for transportation, the U.S. is developing and planning to produce renewable 

energies such as bio fuels on a large scale. Ethanol is one of the bio fuels with potential to promote 

energy independence. In 2006 President George Bush outlined the Advanced Energy Initiative, 

which among other objectives, aims to reduce reliance on foreign energy sources [4]. A study by 

the Department of Energy/United States Department of Agriculture (DOE/USDA) suggests that 

with aggressive technology developments, bio fuels could potentially supply 60 billion gallons1

(238 billion liter) of ethanol by the year 2030, which is 30 percent of the nation’s current use of 

gasoline [4]. The ethanol plan is continued in the current administration.

Ethanol is a high-octane fuel that can be used as a gasoline additive and extender. It can be 

produced by the fermentation of carbohydrates such as sugar, starch, and cellulose. The U.S. 

produces 97 % of its ethanol from corn [5]. In addition, a study by the U.S. Department of 

Agriculture has shown that it is also economically feasible for the U.S. to produce ethanol from 

sugar crops adapted to U.S. weather conditions [6]. After all, the energy requirements for 

producing ethanol from sugar are half the requirements for production from corn. Cellulose 

ethanol production is also underway on a pilot scale. To promote the ethanol industry, the 

government has instituted a subsidy of $0.45 per gallon ($0.11 per liter), although this is set to 

expire in 2010. Imported ethanol is subject to a 2.5 % ad valorem tariff and a secondary tariff of 

$0.54 per gallon ($0.14 per liter). The total tariff for ethanol imported at a price of $2.00 per gallon

is $0.59 ($0.15 per liter). In addition, the depreciation of the U.S. dollar has pushed Brazilian 

ethanol per gallon from $1.10 to between 1.20 and $1.70 even though domestically in Brazil the 

cost has changed little [5].

The process of ethanol production in the plant requires electricity and direct thermal energy. Much 

of this energy is produced by burning fossil fuels. How sustainable is corn ethanol in terms of net 

savings in environmental pollution and carbon emissions in comparison with using pure gasoline?

To answer this question, estimates put the lifecycle reduction in CO2 pollution to be about 20 % in 

comparison with using pure gasoline [7]. In addition, ethanol energy requirements are met using 

coal, natural gas, nuclear, and other sources that are domestically available. Thus, overall ethanol 

contributes to energy independence regardless of its requirements for production. The price of 

gasoline is estimated to be less by between $0.19 and $0.40 per gallon because of the effect of the 

presence of ethanol [8]. This reduction in price benefits motorists directly.

The system dynamics model uses currently available information and forecasts to determine corn 

availability, production capacity, ethanol blend demand, and cost and revenue elements. The 

model can be run using different scenarios of demand and costs of corn, energy, and corresponding 

incomes from ethanol and animal feeds. The different scenarios obtained after model runs are not 

predictions but a starting point to discuss possible alternative outcomes.

1 1 gallon = 3.79 liter
3

1.2 Current state of corn ethanol production

Fuel ethanol is an alcoholic product of fermentation of carbohydrates. It is the same as that 

produced in spirits, wine, beers, etc. but is highly concentrated. Ethanol is produced by process 

type of industry where the materials required for a final product are mixed in given proportions at 

various stages in manufacturing, requires energy inputs, and involves chemical transformation. It 

requires continuous production and the final product is made in bulk. Such industries include 

chemical production, food and beverages, pharmaceuticals, and petroleum products. This is 

contrasted with discrete manufacturing where a product is made or assembled individually. The 

Manufacturing Engineering Laboratory (MEL) at NIST largely researches in discrete 

manufacturing. But, as mentioned earlier, the promotion and production of cleaner fuels like 

ethanol has implications on sustainable manufacturing. Through the Sustainable and Lifecycle 

Information-based Manufacturing (SLIM) project, the MEL is developing information 

technologies and standards-based technologies to product lifecycle problems. The use of ethanol in 

industry and in transportation would reduce manufacturers’ carbon footprint.

There are two major industrial methods for producing ethanol; wet milling and dry milling. Wet 

milling, where the grain is first soaked in water, is used to produce many products such as high 

fructose corn syrup (HFCS), biodegradable plastics, food additives, and corn oil besides the fuel 

ethanol. The process of soaking the grain softens it making it easier to separate the starch, fiber, 

and germ, which are processed separately into the variety of products. This process requires more 

capital investment. With dry milling, on the other hand, the whole grain is processed and the 

residual components are separated at the end of the process [9]. In both processes, enzymes break 

down the starch from the corn into sugar. Ethanol is in turn produced by yeast fermentation of the 

sugars and has to be extracted from the corn mash by distillation. About 70 % of the U.S. ethanol 

is produced by dry milling. Both processes produce animal feeds as useful byproducts.

The corn ethanol produced in the U.S. in 2006 consumed 2.2 billion bushels2 (55.9 million metric 

tons) of corn. To cope with increased demand for ethanol, corn yields are expected to increase. 

Corn available for ethanol production is projected to be 12 billion bushels (305 million metric 

tons) by the year 2030 [11]. This will be against a total corn production projected to be 15.7 billion 

bushels (400 million metric tons) in 2015, 19.2 billion bushels (488 million metric tons) in 2020, 

and 24.6 (625 million metric tons) billion bushels in 2030. Using this information, we can project

the total corn availability for ethanol production each year starting with 2009 when ethanol 

production is expected to be 10.5 billion gallons (39.8 billion liter). Each bushel of corn currently 

yields 2.75 gallons (10.4 liter) of ethanol and 17 pounds (7.7 kg) of dried distiller’s grains with 

solubles (DDGS). The ethanol yield is expected to increase to 2.9 gallons (11.0 liter) per bushel by 

2030. Wet milling also produces corn gluten meal (CGM) and corn gluten feed (CGF). Each liter 

of ethanol consumes 0.18 kWh of electrical energy to produce or 0.69 kWh per gallon. 

Improvements in processing technology are projected to reduce this energy requirement to 0.098

kWh per liter by 2030 [11]. As of 2009 each bushel costs about $4.00 ($158.00 per metric ton)

while ton of DDGS fetches $120.00 ($109.00 per metric ton). CGM and CGF sell for 

approximately $50 per ton ($45 per metric ton). The proceeds from the sale of these byproducts 

contribute to profitability of corn ethanol. The net distribution costs are approximately $0.45 per 

gallon ($0.12 per liter).

2 1 bushel = 25.4 kg
4

The U.S. is now the world’s largest producer of ethanol fuel having produced 9 billion gallons (34 

billion liter) during 2008 and surpassed Brazil that produced 6.47 billion gallons (25 billion liter)

in the same year. The U.S. industry consists of 170 plants operating in 26 states. Increasing 

investments in ethanol production have pushed the installed capacity to 12 billion gallons (45 

billion liter) during the year 2009, although production is estimated to be 10.5 billion gallons (40 

billion liter) [7]. Three plants using sugarcane adapted to U.S. weather were planned to be installed 

in 2009 [6]. The U.S. government is advocating a policy for increased use of ethanol in automotive 

transportation. As of September 2009, there were ten states that mandated a 10 % ethanol mixed 

with gasoline (E10) and federal government wants to increase this blending level to 15 % (E15)

[12]. To understand the implications of further expansion of corn ethanol the structure of the 

industry needs to be investigated. As such, many questions can be asked, for example; what will 

be the effect of additional ethanol production on the price of gasoline? What would happen if 

government subsidy is removed? What will be the effect of water usage during corn production 

and in ethanol processing? What will be the effect of oil prices on the cost-benefit of producing 

ethanol? What would happen if there is sudden increase in demand of ethanol in future compared 

with what is projected? What is the effect on food availability and food prices? A previous model 

seems to suggest that promoting food sources as feedstock for biofuels would lead to increase in 

food prices in the long run [13].

The rest of the paper is arranged as follows. Section 2 introduces system dynamics and describes 

how it can be used to model energy problems. Section 3 describes the causal loop diagram of the 

ethanol system, the supply chain for ethanol, the boundaries of the problem modeled in the paper, 

and the stock and flow diagram. Section 4 is model testing and executing. It also describes the 

model behavior under different scenarios. Section 5 discusses and concludes the paper.

2. Modeling ethanol fuel dynamics

2.1 Introduction to system dynamics

System dynamics modeling was developed by researchers led by Forrester at the Sloan School of 

Management at Massachusetts Institute of Technology in the late 1950s [14]. This was after 

realizing that management sciences were not providing enough understanding of complex systems. 

Forrester defined Industrial Dynamics as “the study of the information-feedback characteristic of 

industrial activity to show how organization structure, amplification, and time delays interact to 

influence the success of the enterprises” [15]. Industrial dynamics later became to be known as 

System dynamics. System dynamics deals with feedback and delays that affect system behavior 

over time. It has had greater use in policy analysis and design. It addresses a problem based on the 

premise that the structure of a system, that is the way the essential system components are 

connected, generates its behavior. Two essential features of the system have to be i) they are 

dynamic and ii) they have a feedback where item x affects another item y and y in turn affects x

perhaps through a chain of causes and effects.

There are two major stages in developing a system dynamics model. The first stage is to construct

building blocks relating to the cause and effect of the behavior of factors in the system. This is 

called the causal loop diagram (CLD). With the CLD you can graphically visualize the 

interrelationships between different factors relevant to the system. The factors are connected by 
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links, which are often drawn as arrows. If an arrow goes from A to B it indicates that factor A 

causes factor B. A polar sign is added between connecting arrows to indicate the direction that one 

factor influences another. A positive sign indicates that one factor influences the other positively, 

while a negative sign means that the factor influences the other negatively. The second stage is to 

develop a quantitative model and represent it in terms of flow rates, levels and delays. And most 

importantly, studying the link between x and y independent of other links between y and x cannot 

predict how the system will behave. Rather the study of the system as a whole with its feedback 

links can lead to correct conclusions and understanding. System dynamics involves studying the 

impact of delays on system behavior. Specifically, what are the implications when a cause takes a 

long time to exert its effect? System dynamics has been used to model dynamic systems with 

different time and spatial scale [16]. It can model the behavior of systems in diverse areas such as 

economics and other social sciences, supply chain, education policy, biology, and engineering. A 

number of systems dynamics simulation tools such are available on the market.

2.2 System dynamics and energy modeling

One of the earliest major applications of system dynamics in policy oriented modeling was the 

World3 model, published in 1972 [17]. The World3 model preceded Forrester’s World2 model of 

1971. These models were used to predict that the current usage and depletion of natural resources 

as the basis for economic growth, coupled with exponential population growth will impose limits 

to physical growth sometime during the 21st century. More than 30 years after publication of 

World3 a new version called World3-03 was developed. It included more scenarios than previous 

models. This model is explained in “Limits to Growth – The 30 Year Update” [18]. This model 

explains that the current unprecedented growth in population, food production, resource 

consumption, and industrial production cannot be sustained in the long run without exceeding the

earth’s limits. If this ever happens, the natural system, industrial and agricultural production, and 

human population would inevitably collapse. However, the model shows that if humanity adopts 

technology that uses renewable resources, controls population growth, and reduces pollution, this 

collapse would be avoided and the resulting world would sustain high living standards and human 

welfare. Early energy models such as those developed by Naill [19] and Sterman [20] were used to 

model fossil fuel usage. An investigation and modeling framework for transition to bio fuels has 

been developed [21]. The outcome of such models can be used to determine and direct energy 

policy to help alleviate potential economic and social problems. 

System dynamics models have been developed in a number of bio fuel situations but overall the 

literature available on the subject is still relatively scanty. In the Noblis corporate energy initiative, 

it is proposed that system dynamics could aid in assessing energy supply-chain scenarios to 

manage the transition to a more sustainable energy future [22]. Bush et al. [23] use system 

dynamics to explore potential market penetration scenarios for bio fuels in the U.S. The 

investigation into bioenergy and land use to understand the interaction between economic 

conditions and land competition between different crops using system dynamics is done by 

Scheffran et al. [24]. While Franco et al. use system dynamics to understand the difficulties in 

fulfilling government requirements for biofuels blending and to evaluate the effect of different 

government policies in the production of ethanol and biodiesel [25]. A systems dynamics 

modeling in the U.S. Department of Energy biomass program is described in Riley et al. [26]. 
6

Regarding ethanol modeling, West [27] along with other researchers at Sandia National 

Laboratories and General Motors, describe a system dynamics model to investigate the feasibility, 

economics and environmental impact of producing 90 billion gallons (341 billion liter) of ethanol 

per year by 2030. There is also a system dynamics model for forecasting the future of bio diesel 

production growth in the U.S. [28]. This model has conducted sensitivity analysis to determine the 

parameters that most affect the feasibility, cost competitiveness, and greenhouse gas impact of 

large scale bio diesel production. This model investigates the effect of several key factors such as 

market growth, government incentives, stock prices, etc. on the viability and sustained growth of 

the industry. Our motivation for corn ethanol is similar to that of this bio diesel research. 

3. Model conceptualization

This section discusses the causal loop diagram and stock and flow diagrams of the corn ethanol 

system. We used the system dynamics modeling tool Vensim, which allows the analyst to connect 

words with arrows representing relationships between variables and recorded as causal 

relationships. The stock and flow diagram of the ethanol system in Vensim is made up of variables 

- auxiliaries or constants, box variables that represent stocks/accumulations [29]. This information 

is used by the equation editor to complete the modeling process.

3.1 The causal loop diagram

Figure 1 shows the causal loop diagram of the corn ethanol industry system. These factors show

the interactions and relationships between the corn ethanol industry and the overall transportation 

energy, corn production, and food systems. The diagram comprises of multiple loops that show 

how corn production, corn price, level of ethanol production, government tax incentives and oil 

price influence the industry. We examine a loop that starts with the Corn ethanol production

factor, as an example. An increase in Corn ethanol production influences a reduction in Ethanol 

price. But an increase in Ethanol price decreases Ethanol demand, hence the negative polarity of

the connecting arrow. Since we have a decrease in Ethanol price there will be an increase of 

Ethanol demand followed by the Gap between ethanol demand and supply. This will call for both 

increased Ethanol production investment and demand for more Allocation of corn to ethanol 

production. An arrow closes the loop with positive polarity since an increase in Allocation of corn 

to ethanol production means an increase in Corn ethanol production. This loop represents an 

example of positive feedback self-reinforcing process. However, the loop would be prevented 

from increasing the levels of each factor indefinitely because other factors outside the loop such as 

Total production costs actually influence the Ethanol price. Gasoline price also influences Ethanol 

demand. Another example starts with Profitability, which when it increases will positively affect 

further Ethanol production investment and Allocation of corn to ethanol production. The effect of 

this is to increase Corn ethanol production and decrease the Ethanol price by the forces of supply 

and demand. A lower Ethanol price would mean reduced Profitability. This is an example of a self 

balancing loop, where growth is attenuated and checked from within the loop. Such a subsystem 

would tend to be innately stable.
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Corn ethanol

production

Corn

production

Available

agricultural farmland

Agricultural

efficiency

Corn yield + per acre

Farmland for

corn farming

-

-

Corn price

-

Ethanol production

efficiency

Ethanol produced

per bushel of corn

+

Ethanol price

Gasoline price

-

Profitability

Incentives

+

Ethanol

demand

+

Total

production costs

-

Oil price

+

+

Ethanol production

investment

Gap between ethanol

demand and supply

+

-

+

Allocation of corn to

food and animal feed

Allocation of corn to

ethanol production

Human corn food

and animal feed price

+

-

+

Demand for allocation

of corn to ethanol

Demand for corn for

human food and animal

feed

Gap between demand and

- corn allocated for food

+

Gap between demand

and corn allocated to

ethanol

-

+

+

+

Gasoline

demand

-

-

+

Environmental

pollution

Transportation

costs

+ +

Water and energy

usage at farm

+

DDGS and CGF/CGM

(animal feed) revenue

+

Poduction water

costs

+

Production

energy costs

+

+ +

-

+

+

+

+

+

+

Water and energy

costs at farm

+

+

+

+

Other operating

costs

+

+

+ -

-

+

+

+

+

Figure 1: Causal loop diagram of the dynamics of corn ethanol

The key factors relevant to the level of ethanol production are Ethanol production investment (in 

terms of plant) and the Allocation of corn to ethanol production. The profitability depends on 

Total production costs, Ethanol price, Revenue from animal feeds, and government Incentives. 

Corn price plays a role in total production costs. Figure 1 also shows the relationship between 

factors that influence the amount of corn produced, farmland, water usage, environmental 

pollution, allocation between competing demands, and the corn price.

3.2 Model overview

We review the flow of corn from farms to ethanol in the dispensing fuel pumps. Figure 2 is a 

schematic representation of stock, product, and information flow in the corn ethanol industry. 

Farm equipment is required to clear the land, cultivate, plant, grow, and harvest the corn. The 

farmers use fertilizers that can release nitrous oxide into the atmosphere. Farming also uses water 

for irrigation although in the U.S. only a small fraction of corn farming uses irrigation. Corn 

transportation to plants and ethanol production activities use fossil fuels contributing to demand of 

these fuels and also increasing carbon emissions into the atmosphere. The corn produced is used as 

animal feeds and human food, for ethanol production, and for other industrial applications. Some 

corn produced in the U.S. is exported. Ethanol has also to be transported from the plants to 

distribution centers around the country, mostly using tracks. An improved infrastructure where rail 
8

is used would reduce transportation costs. The quantity of ethanol to be produced would influence 

and be influenced by investment in the infrastructure. The ethanol is used in blended form, with 

gasoline, whose supply is assumed to be constant. The sale of animal feeds byproducts of ethanol 

production contributes to overall revenue. The model boundary indicated in Figure 2 shows the 

core part of the ethanol production and distribution system that is included in the stock and flow 

diagram model [10].

Figure 2: Overview of the corn ethanol production and distribution system

3.3 Stock and flow diagrams

The data and relationship between factors was based on projected corn availability and investment 

in the industry [11]. The estimates are aggregated for the entire corn ethanol industry in the U.S. 

There are four sectors in the model (Figures 3 to 6). Figures 4 to 6 are shown in the Appendix. The 

first is the main corn ethanol production sector that depicts the supply and demand dynamics of 

corn and ethanol. The second, called secondary production, models animal feeds production as a 

byproduct of ethanol production. The third sector models the energy used during production. The 

fourth sector models the capital flows of income and expenditures. The forecast horizon starts 

from 2009 to 2030. But we use a month as the time unit and the model performs computations 

every 1/8th of a month. We use previous research data and projection into the potential role of corn 

ethanol in meeting energy needs of the U.S. [11].

Corn Ethanol 

Production

Corn Farming

Forecast

Water Sources

●Water Tables

●Rivers

Food Market

●Human,

●Animal feed

Energy Market

●Industrial Production

●Transportation

●Heating, Living

Energy Sources

●Natural Gas

●Electricity

●Coal

Blending

and Selling

CO2, N2O

Release

Gasoline Supply

- Assumed constant

Corn Transport

Harvest

Ethanol Transport

Investment 

Capital

Model boundary

CO2 Release
9
10

Using this information, we model the ethanol production sector. We use the forecast for corn 

harvest over the modeling horizon, and by linear interpolation determine the total corn production 

each year between 2009, when production will be 13.1 billion bushels (333 million metric tons), 

and 2030 where projection puts production at 24.6 billion (625 million metric tons). With this 

information, the corn available for ethanol production would grow from 4 billion bushels (102 

million metric tons) during 2009, representing 30 % of total corn production, to 12 billion bushels 

(305 million metric tons) in 2030, representing 49 % of total corn expected to be produced. 

We assume the amount of ethanol to be mixed with gasoline before sale will depend on the 

demand. This is a “pull” system. The corn harvest system, on the other hand, is modeled as a 

“push” system where corn is harvested as projected and stored. In case the demand for corn is less 

than that anticipated, it would accumulate in the reserve. If the demand happens to be higher than 

what is projected and the production plant capacity is available, there would be no corn left in 

reserve as it will all be consumed. In this case, corn availability would be the constraining factor in 

ethanol production. The demand for corn for other purposes, i.e., animal feed and human food, 

other industrial applications, and export are modeled according to the projection. The price of corn 

depends on the rate at which corn is purchased for ethanol production and the rate at which it is 

harvested. If the purchase rate increases the price goes up and vice versa. The available corn stock 

and harvest also affect the price. If the stock is low the price increases.

The demand for ethanol and production plant capacity increases in the same proportion as the 

amount of corn harvested. The current proportion of production from the two types of mills, i.e.,

70 % by dry milling and 30 % by wet milling is used in the model. However, in a given period, not 

all orders for ethanol may be satisfied since the ethanol delivered will depend on stock level and 

production during the period. The price of ethanol blend also depends on the demand.

Corn is purchased for ethanol production depending on the demand, price, and corn availability. 

The corn demand depends on production requirements and maintenance of stock reserve. This

stock level of corn to maintain, in turn, depends on the desired ethanol production, buffer stock 

coverage, and conversion rate of the mills. Buffer stock coverage is measured in a number of 

months supply. The ethanol production rate depends on the demand and to fill the reserve stocks 

for each blend that should be maintained. Two other factors affect the production rate: the installed 

capacity of the processing mills and the availability of corn. At any given time, one of the three 

constraining factors above would determine the quantity of ethanol produced before next 

computational step (called “Time Step”). 

We model two blends of ethanol; the E10 and E85 to constitute 80 % and 20 % of total demand

respectively. This distribution of demand for different blends has a consequence on the ethanol 

required from the production system. For example, 1000 liter of blended fuels would constitute

200 liter of E85 and 800 liter of E10. The 200 liter of E85 would contain 170 liter of ethanol while 

the E10 would have 80 liter. This implies a total of 250 liter of corn ethanol required. Therefore,

for any quantity demand for the blend fuel, the volume of ethanol required would be one quarter of 

the total blend. The supply of gasoline for blending with ethanol is assumed to be constant. The 

drivers of the model are demand and production of ethanol. The demand for the blend in the model 

represents total demand of ethanol already mixed with gasoline in the various proportions 

described earlier in this paragraph.
11

The secondary production, i.e., for DDGS and CGF/CGM, as a byproduct of ethanol production, is 

shown in Figure 4. This sector model also includes water usage. CGF/CGM is only produced from 

the wet mills. The rate of production of these products depends is factored into the quantity of 

ethanol produced. The price of these products affects the quantities sold.

Figure 5 shows energy utilization in production. Electricity is used for lighting and running 

machinery and equipment. The heating is provided by natural gas and coal on a 50-50 basis in 

terms of the heat produced from the energy source but not necessarily that the cost is the same. 

Electricity consumption is measured in kWh. Natural gas is priced in dollars per British Thermal 

Unit (BTU) (1 BTU = 1055 J) but coal is priced in dollars per ton. The price of coal generally 

differs depending on the quality of the coal. We assume an average price for coal. To represent 

quantity of coal and natural gas with a single unit of measure in the model, we determine the 

BTUs available in one ton of coal (Joules in one metric ton of coal). The capital flow model of 

Figure 6 shows (Appendix) the income and expenditures associated with ethanol production. 

4. Running the model on the ethanol production sector

The validity of the structure of the model was first checked for dimensional consistency. The 

model was then executed. Corn ethanol production on a large scale as a transportation fuel is a 

recent development in the U.S. with no significant historical data. The unit of time used in 

modeling is one month and the model is run for a period of 21 years (252 months) into the future. 

The “base” run of the model is set at forecast levels of ethanol demand, ethanol prices, corn 

production, and plant production capacity. After the base run, the model is executed using a new 

set of scenarios to view the behavior. Figures 7, 8 and 9 show what happens when there is a 

change from the projected levels. In the first case, it is assumed that half way through the 252 

months simulation, the demand changes while the other factors remain as previously projected. In 

the second case, the production plant capacity changes while the other factors including demand 

remain unchanged. In the third, we run a scenario where the demand, production plant, and corn 

harvest change by the same percentage. 

4.1 Change in demand

Figure 7 graphs show the variation of selected factors and stocks during a simulation run. It shows 

the parameters: Ethanol fuel mixture demand, E10 stock, Selling and using E10, Ethanol stock, 

Desired production, Corn demand for ethanol, Corn purchase for ethanol, and Corn stock 

available. An initial stock of 4 billion gallons (15.2 billion liter) of E10 blend is assumed but the 

rest supply line for ethanol and corn for ethanol production is empty. The initial sales are supplied 

from the stock. This causes the sudden drop in the stock as shown in Graph 7B. When this stock is 

depleted the sales also drop. This is shown in Graph 7C. The beginning of a run also shows sudden 

increase in desired production, demand for corn, and corn purchase. This is reflected in Graphs 7E, 

7F, and 7G respectively. As the simulation executes, the corn and ethanol begin to flow through 

the system, the E10 stock builds up. The sale of E10 rises until it catches up with the demand. 

There is no backlogging of ethanol demand and any unfulfilled demand is lost. E10 stock will also 

rise as reflected in Graph 7B until it reaches the predetermined reserves of 4 billion gallons. At this 
12

time, no further rise in E10 stock is seen in the graph. This is because we used the rule that the 

blending rate is to satisfy the need for the buffer stock and to satisfy the demand for the ethanol 

blend. Further production of ethanol beyond these requirements will lead to a buildup of the 

ethanol stock. This accumulation of ethanol leads to a slight reduction in the rate of corn purchase

around week 70, shown in Graph 7G. The corn in stock begins to rise since the corn harvest rate 

remains unchanged (Graph 7H).

Graph 7A shows the 20 % change in projected demand. Correspondingly, the sales increase as 

shown in Graph 7C, but for a short time before falling. This is because the additional demand is 

satisfied from stock. The blend and ethanol stocks also drop, as shown in Graphs 7B and 7D. 

When this stock is depleted the sales reduce to a level commensurate with the rate of production of 

ethanol, which is constrained by the capacity of the mills and corn availability. The Graphs 7E and 

7F show the increase for Desired production and Corn demand. The Corn purchase, shown in 

Graph 7G, increases steeply reflecting the increase in demand but drops suddenly to previous 

level. This is because the increase is satisfied from the Corn stock available until it is depleted as 

shown in Graph 7H.

4.2 Change in plant capacity

Next is the simulation execution when only the production capacity surges by 20 % during month 

126. The results of this scenario are shown in the graphs of Figure 8. Since there is no change in 

demand the sales remain unchanged as shown in Graphs 8A. However, the Ethanol production 

increases due to increase in capacity. This is shown in Graph 8D.Therefore, the Ethanol stock 

increases sharply (Graph 8B). The increase in ethanol stock up to a given level reduces the desired

production for ethanol, which Graph 8C indicates. Because the ethanol production had increased, 

the Corn demand and Corn purchase for ethanol also went up. This is shown in Graphs 8E and 8F. 

However, the Corn purchase is short lived since the Corn stock for ethanol production and Corn 

stock available are soon depleted as Graphs 8G and 8H, respectively show. We also note that the 

increased ethanol production lasts only as long as the corn in stock is available. Once the stock, 

previously accumulated, is used up the corn purchase and ethanol production revert to base levels. 

The net result of the surge happens to be the depletion of Corn reserves and increase in ethanol 

stock.

4.3 Change in demand, plant capacity, and corn production

The graphs in Figure 9 are the results of running a scenario where the demand, production 

capacity, and corn availability were increased simultaneously by 20 % during the month 126. The 

blend stocks would be maintained and the ethanol stock would increase slightly. This is shown in 

Graphs 9B and 9C. The Ethanol production, Corn demand for ethanol, Corn purchase for ethanol, 

and Corn stock for ethanol also increase in correspondence with the 20 % rise (Graphs 9D, 9E, 9F, 

and 9G respectively). Lastly, since the increase is in the corn harvest and ethanol demand without 

increase in other uses of corn, the Corn stock available increases (Graph 9H).
13

 Graph 7A Graph 7B

 Graph 7C Graph 7D

 Graph 7E Graph 7F

 Graph 7G Graph 7H

Figure 7: Comparison between base run and surge in demand (1 gal = 3.79 l, 1 bushel = 25.4 kg)

Ethanol fuel mixture demand

20 B

15 B

10 B

5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal/Month

Ethanol fuel mixture demand : Demand rise

Ethanol fuel mixture demand : Base run

E10 stock

6 B

4.5 B

3 B

1.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal

E10 stock : Demand rise

E10 stock : Base run

Selling and using E10

10 B

7.5 B

5 B

2.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal/Month

Selling and using E10 : Demand rise

Selling and using E10 : Base run

Ethanol stock

10 B

7.5 B

5 B

2.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal

Ethanol stock : Demand rise

Ethanol stock : Base run

Desired production

20 B

15 B

10 B

5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal/Month

Desired production : Demand rise

Desired production : Base run

Corn demand for ethanol

2 B

1.5 B

1 B

500 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh/Month

Corn demand for ethanol : Demand rise

Corn demand for ethanol : Base run

Corn purchase for ethanol

1 B

750 M

500 M

250 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh/Month

Corn purchase for ethanol : Demand rise

Corn purchase for ethanol : Base run

Corn stock available

2 B

1.5 B

1 B

500 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh

Corn stock available : Demand rise

Corn stock available : Base run
14

 Graph 8A Graph 8B

 Graph 8C Graph 8D

 Graph 8E Graph 8F

Graph 8G Graph 8H

Figure 8: Comparison between base run and surge in production capacity

(1 gal = 3.79 l, 1 bushel = 25.4 kg)

Selling and using E10

10 B

7.5 B

5 B

2.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal/Month

Selling and using E10 : Capacity rise

Selling and using E10 : Base run

Ethanol stock

40 B

30 B

20 B

10 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal

Ethanol stock : Capacity rise

Ethanol stock : Base run

Desired production

10 B

7.5 B

5 B

2.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal/Month

Desired production : Capacity rise

Desired production : Base run

Ethanol production

4 B

3 B

2 B

1 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

Ethanol production : Capacity rise

Ethanol production : Base run

Corn demand for ethanol

2 B

1.5 B

1 B

500 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh/Month

Corn demand for ethanol : Capacity rise

Corn demand for ethanol : Base run

Corn purchase for ethanol

1 B

750 M

500 M

250 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh/Month

Corn purchase for ethanol : Capacity rise

Corn purchase for ethanol : Base run

Corn stock for ethanol

6 B

4.5 B

3 B

1.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh

Corn stock for ethanol : Capacity rise

Corn stock for ethanol : Base run

Corn stock available

2 B

1.5 B

1 B

500 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh

Corn stock available : Capacity rise

Corn stock available : Base run

gal/Month
15

 Graph 9A Graph 9B

 Graph 9C Graph 9D

 Graph 9E Graph 9F

 Graph 9G Graph 9H

Figure 9: Comparison between base run and simultaneous increase in corn production, plant 

capacity, and demand (1 gal = 3.79 l, 1 bushel = 25.4 kg)

Selling and using E10

20 B

15 B

10 B

5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal/Month

Selling and using E10 : Three factor rise

Selling and using E10 : Base run

E10 stock

6 B

4.5 B

3 B

1.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal

E10 stock : Three factor rise

E10 stock : Base run

Ethanol stock

20 B

15 B

10 B

5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

gal

Ethanol stock : Three factor increase

Ethanol stock : Base run

Ethanol production

4 B

3 B

2 B

1 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

Ethanol production : Three factor increase

Ethanol production : Base run

Corn demand for ethanol

2 B

1.5 B

1 B

500 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh/Month

Corn demand for ethanol : Three factor rise

Corn demand for ethanol : Base run

Corn purchase for ethanol

2 B

1.5 B

1 B

500 M

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh/Month

Corn purchase for ethanol : Three factor increase

Corn purchase for ethanol : Base run

Corn stock for ethanol

6 B

4.5 B

3 B

1.5 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh

Corn stock for ethanol : Three factor increase

Corn stock for ethanol : Base run

Corn stock available

40 B

30 B

20 B

10 B

0

0 28 56 84 112 140 168 196 224 252

Time (Month)

bsh

Corn stock available : Three factor increase

Corn stock available : Base run

gal/Month
16

4.4 Overall assessment

These preceding simulation runs show model behavior and factor variations that would not be 

apparent without the model. As an example, the system behavior in the first few months before 

steady state conditions are achieved would have been difficult to foretell without the model. 

Another is the relatively short period of time for which a surge in demand can be satisfied before 

production capacity and/or corn availability force the sales to drop to base values. Additional 

experiments can be performed to determine the behavior using alternative inputs or if we have a 

reduction in inputs rather than an increase. Other inputs such as change in prices of ethanol blend 

or corn, a drop in the availability of corn, or improvement in conversion rate of the mills could be 

used to determine their effect. 

We keep in mind that the relationships between factors such as determination of prices, or between 

demand for ethanol and demand for corn in ethanol production are set by intuition in this model. 

Different analytical expressions can be developed after further research into the interaction 

between these factors and the application of sophisticated mathematical analysis. Secondly, the 

model is relatively complex and many factors can be used as the basis for comparison of different 

set scenarios. The demonstrations delved in factors from the main corn ethanol production system. 

Factors from the remaining sectors can also be used. In addition, other ethanol blend fuel 

combinations of demand can likewise be investigated.

5.0 Discussion and conclusion

The paper has described a system dynamics model that can be used to study the state of corn 

ethanol fuel industry in the U.S., and the effect of changes to the forecast future trend on the 

operating parameters. The importance of energy independence cannot be overestimated. Oil prices 

are expected to rise in the years ahead as reserves are depleted and the process of extraction of the 

remaining oil becomes more expensive. Corn ethanol is one of the biofuels currently promoted 

since corn can be readily grown. As a consequence, corn production is projected to increase and 

more processing plants are being set up. 

Ethanol for transportation purposes is used in blended form with gasoline, in general. It can be 

used to oxygenate gasoline as an alternative to methyl tertiary butyl ether (MTBE) and as an 

extender. Improvements in farming technology are expected to increase the number of bushels 

produced per acre grown. The ethanol produced per bushel of corn is also expected to increase 

while electrical and direct thermal energy required per gallon of ethanol is expected to decrease. 

Nonetheless, promoting corn, which is a food source to be an alternative form of energy, is 

believed to lead to increase in food prices. There is also a question of what would happen to the 

corn ethanol industry as prices of ethanol fluctuate in response to availability and supply of 

alternative transportation energy sources. There is also need to be able to investigate the situation 

where there are changes from projected corn production, corn prices, capacity, demand and other 

factors relevant to the industry. This type of problem was seen to be amenable to system dynamics 

modeling, a tool that has proven effective in holistic modeling of dynamic systems. The model 

developed includes four sectors: the main corn ethanol production, the secondary production, the 

energy usage, and the capital flow. The data used is based on literature. One of the major 

challenges was determining functional relationships between the factors. The other challenge was 

the determination of and inclusion within the model, of operating policies and decision rules. 

Executing the model has shown how it responds to independent changes in demand and capacity. 
17

It has also been demonstrated what would happen if three factors are changed simultaneously. The 

model can be used as a methodological tool to conduct sensitivity analyses on other issues. 

Additional information about the industry can be used to improve the model. The model can also 

be extended to include factors such as the current and future supply of other alternative fuels and 

petroleum.

Models are as useful as the data used in their construction and the analysts’ understanding and 

inclusion of important and relevant factors. However, production of bio fuels on a large scale is a 

new undertaking in the U.S. with more studies in the literature making future projections than 

those reporting past historical data. As such, the model is useful in exploring changes in expected 

trends. This model can be extended to include the entire bio fuel industry. In this mode, it would 

provide a holistic picture and provide an investigative tool of the future impact of bio fuels on 

transportation in the U.S.

ACKNOWLEDGMENT

Work described in this report was sponsored by the Sustainable and Lifecycle Information-based 

Manufacturing (SLIM) program at the National Institute of Standards and Technology (NIST), 

Gaithersburg, Maryland. The SLIM program applies information technologies and standards-based 

approaches to manufactured product lifecycle problems. The initial study was carried out by 

Stephen Nowak under the Summer Undergraduate Research Fellowship (SURF) program, to 

which this research is indebted. The work described was funded by the United States Government 

and is not subject to copyright.

DISCLAIMER

A number of software products are identified in context in this paper. This does not imply a 

recommendation or endorsement of the software products by the authors or NIST, nor does it 

imply that such software products are necessarily the best available for the purpose.
18

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Appendix

Figure 4: The stock and flow diagram of the animal feed byproducts production

Figure 5: The stock and flow diagram of the energy use
22

Figure 6: Stock and flow diagram of the capital flow