bioenergy efficiency and a flex-mill simulation in mato grosso · bioenergy efficiency and a...

Campo Grande, 25 a 28 de julho de 2010,

Sociedade Brasileira de Economia, Administração e Sociologia Rural

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BIOENERGY EFFICIENCY AND A FLEX-MILL SIMULATION IN MATO GROSSO

[email protected]

APRESENTACAO ORAL-Economia e Gestão no Agronegócio WALDEMAR ANTONIO DA ROCHA DE SOUZA1; PETER GOLDSMITH2; JOÃO

GOMES MARTINES FILHO3; RENATO LIMA RASMUSSEN4. 1,3.USP ESALQ, PIRACICABA - SP - BRASIL; 2.UNIV. OF ILLINOIS AT URBANA-

CHAMPAIGN (UIUC), URBANA; 4.UIUC, URBANA.

Bioenergy Efficiency and a Flex-Mill Simulation in Mato Grosso

Grupo de Pesquisa: Economia e Gestão do Agronegócio

Resumo Os combustíveis baseados em biomasa líquida (ou simplesmente “biocombustíveis”) têm chamado atenção especial como uma alternativa factível para o setor de transportes. O conceito analítico de giro de ativos é uma alternativa e possivelmente uma maneira mais eficiente para avaliar os impactos sobre a competitividade e a viabilidade de longo prazo de modelos energéticos alternativos. Esta pesquisa apresenta um estudo de caso de extensão do período de operações de uma usina de cana, destacando-se as principais conclusões de operação anual plena da usina, múltiplas fontes de grãos, custos de adaptação e questões de flexibilização. Palavras-chaves: Biocombustíveis, flex-mill, milho, cana-de-açucar, Mato Grosso.

Abstract

Liquid biomass based fuels (or simply “biofuels”) has captured particular attention as a feasible alternative for fossil transportation fuels. The analytical concept of asset turns is an alternative and possibly better way, to assess the competitiveness and the long term viability implications of alternative energy models. This research presents a case study of extending the operating season of a sugarcane mill, with main findings on the full year mill operation, multiple feedstocks, switching costs and flexibility issues. Key Words: Biofuels, flex-mill, corn, sugarcane, Mato Grosso.

1. Introduction

Until the 70’s renewable energy production systems were widely considered minor

and declining sources for energy (Maugh, 1972). Since then, oil price shocks, concerns about finite energy dependence, technological advances, varied and new renewable-related



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energy-products, green job creation, rural economic development, have changed the policy and business environment for renewable energy in the economy (Cowan, 2003; Sims, et al., 2006; Petroleum, 2009; Pandey, 2009).

Rapid development by some of the most populated countries has significantly tightened fossil fuel supplies causing prices to rise dramatically over the last fifteen years. Concerns over climate change have accelerated the interest in renewable energy sources in general, and liquid biofuels in particular (McKendry, 2002a; Sims et al., 2006). Governments have reacted to these concerns by introducing financial frameworks and policies to mobilize capital investment in renewable energy projects. The United States invested almost $25 billion dollars, 20% of the global investment, into the sector in 2008 (Petroleum, 2009).

Renewable energy growth has not been restricted to developed countries. At least 64 countries had policies to promote renewable power generation by early 2009 (REN21, 2009). Global annual renewable energy investment has globally increased 400% since 2004 and reached $120 billion dollars in 2008. Solar photovoltaic annual capacity increased 500% to 16 gigawatts, wind-power 250% to 121 gigawatts, and ethanol production 100% to an annual capacity of 70 billion liters. Total energy production from renewables increased to 280 gigawatts during the period.

Global annual energy demand could jump from 400 EJ in 2000 to 700-1000 EJ by 2050 and reach 1275-1500 EJ/year in 2100 (Clarke et al., 2007). Demand pressures and climate change concerns will likely continue shift policy and investment away from traditional sources, such as fossil fuels, and towards having renewables play a larger role in the world’s energy portfolio.

2. Case Study Background

Within this settings, liquid biomass based fuels (or simply “biofuels”) has captured

particular attention as a feasible alternative for fossil transportation fuels (McKendry, 2002; Sims et al., 2006). Liquid fuel bioenergy also offer opportunities in a context of technical standards and physical characteristic that allow for gradual and smooth transition from petroleum to bio based fuels. Liquid fuel bioenergy models (LFBM), such as the United States maize model and the Brazil sugarcane model dominate the current liquid biofuel landscape with ethanol. The production technology’s relative simplicity and the fact that ethanol fuel can be easily blended or utilized in pure form have contributed to this early leadership position (Cardona & Sánchez, 2007). These first generation models produce significant quantities of biofuels very rapidly, but have been criticized for poor system efficiency (Macedo, 2007; Capehart, 2007; Goldsmith, et al., 2009; Da Rosa, 2009). Second and third generation alternatives involving cellulosic and other higher density and waste feedstocks may significantly improve system efficiency but still require significant investments in research and development prior to being ready for commercialization (Sims et al., 2006; Hamelinck and Faaij, 2006).

In previous research we introduced the analytical concept of asset turns as an alternative and possibly better way, to assess the competitiveness and the long term viability implications of alternative energy models (Goldsmith et al, 2009). Energy asset



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turns is the ratio of energy quantity to the underlying assets used along the bioenergy value chain from production through to utilization (Goldsmith et al, 2009). Liquid Fuel Bioenergy Models though in general involve production systems that are frequently spatially disperse and involve types of fuels and feedstocks thus have relatively low energy densities or are limited in the percentage of energy that can be readily captured. Ceteris paribus this leads to inefficient use of assets and poor system efficiency. At the same time, use of renewable and low cost feedstocks helps compensate for weak density attributes. Multiple feedstock usage, the subject of this manuscript, is one way to improve capital utilization efficiency. Doing so helps to compensate for low energy density properties and poor capital utilization elsewhere in the system. The flex mill concept is hypothesized to allow sugarcane ethanol mills to compete more effectively with traditional and other alternative fuel models by allowing operations a second feedstock; maize.

Consider the Midwest maize LFBM, the dominant commercial system in place in the United States. In a stylized form, maize is grown in a 75 kilometer radius of the dry mill. The grain is harvested and transported or stored. The mill operates 350 days a year (Eidman, 2007). The ethanol, 1/3rd of the mill’s output and 100% of the energy output, is transported by truck or rail to refineries for blending with gasoline and entrance into the transportation fuel supply channel. The maize yields about 400 liters per ton of maize. Mill locations are generally distant from refiners and high population centers. Dried distillers grains and solubles, a third of the mill’s output, are shipped to local feed mills or livestock producers, often close to ethanol plants, as a medium protein feed product. The final third of a dry ethanol mill’s output is carbon dioxide which is either vented or sold into industrial marketing channels. Low energy productivity, high degrees of spatial separation, and high energy demanded for product and by product transformation decrease the energy asset turns for the Midwest maize system. On the other hand the plants operate continually and many system assets are not dedicated exclusively to only ethanol production, which improves capital use efficiency.

Brazilian sugarcane-to-ethanol models harvest a wet feedstock and transport the material directly to a processing facility where it is converted into either sugar or alcohol (Macedo, 2007). Plants continually alternate between sugar and ethanol production simply as a function of the relative wholesale prices (Martines et al., 2006). In Mato Grosso, sugarcane is produced, harvested, and transported 25 kilometers directly to the mill (PECEGE, 2009). Sugarcane perishes quickly so there is no option for storage. The logistics must be in place for continuous harvesting and delivery of cane (Iannoni & Morabito, 2002). Plants operate for only about 200 days a year, the length of the harvest. A metric ton of sugarcane yields about 90 liters of ethanol (CONAB, 2008; PECEGE, 2009). Additional by-products are 135 kilograms of dry cellulose-laden bagasse, which is burned for electricity, and 605 kilograms of water (Da Rosa, 2005).

The objectives of this research are to use the Mato Grosso sugarcane setting to analyze the novel flex-mill approach and simultaneously understand the broader concept of asset utilization within liquid biofuel production systems.

3. Case Study Setting



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The state of Mato Grosso State is located in Brazil’s Mid-Western Region. It has 11 mills in operation that utilize 3.3% of Brazils sugarcane crop (IBGE, 2007). The lower land costs, good sugarcane growing environment, and low cost maize availability create the opportunity to increase the ethanol production based on both raw-materials. Ethanol has been produced in Mato Grosso State for the last 30 years. Most of the mills started to operate during the Pro-Alcool Program when the government subsidized the energy sector in Brazil in response to the oil embargo of the 1970’s.

The abundant supply of maize, poor transportation infrastructure, long distances to markets, and an infant local agro-industrial complex create a weak basis for maize. On the other hand soybeans and maize are complements in Mato Grosso. Producers successfully employ a soybean-maize succession cropping plan, whereby a soybean and maize harvest occur in the same year. Mato Grosso is the largest producer of soybeans in Brazil.

Ethanol produced from sugarcane is about 40% less expensive to produce than when produced from maize; $0.87US/gallon versus $1.51US/gallon (LICHT, 2006). Nonetheless sugarcane plants are idle a significant portion of the year as sugarcane cannot be stored, while maize is a highly storable and transportable feedstock.

The mills in Mato Grosso State own approximately 70% of land used for the sugarcane production. Sugarcane prices in Mato Grosso State were around R$35.00/ton for the 2006/07 crop year (Personal Communication, 2007). In comparison, the sugarcane price for a medium size plant in Sao Paulo State reached R$44.85/ton during the same period. Distance to markets reduces the value of ethanol compared to those regions more closely situated near population centers. Plants located in Sao Paulo State, the main center of ethanol production in Brazil, lease land from farmers or directly purchase feedstock, rather than owning land assets as is practiced in Mato Grosso (PECEGE, 2009).

Maize is planted after the soybean harvest in February (Figure 1). Mato Grosso State produces 6.1 million tons of maize and is the nation’s second largest producer. Between 2005 and November 2007, the average price in the Chicago Board Trade (CBOT) was $2.70 per bushel. The maize prices in Mato Grosso average about $0.85 per bushel or and 69% less than the CBOT (Figure 2). At the same time, the prices in Campinas, in eastern state of Sao Paulo where basis is stronger, receive $1.33 per bushel or a 56% premium over Mato Grosso.

Sugarcane plants in Mato Grosso are idle approximately four months out of the year. Ethanol does not store well because of its physical property to absorb water. For similar reasons dedicated transport and transfer facilities are required; raising capital costs per unit of energy. Finally ethanol energy density is significantly less than other liquid fuels such as gasoline or butanol. The low energy density and high cost of handling and storage reduce the efficient use of capital with in ethanol systems (Goldsmith et al., 2009).

The demand for ethanol is year-round, though the supply of sugarcane is not. The truck transportation sector imports all transportation fuel used in the State, except the 800 million liters produced by its small sugarcane ethanol industry.

4. Method



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We simulate the production of ethanol from both sugarcane and maize during the 2004 to 2007 period using a mix of primary plant-level and secondary time series data. We analyze weekly gross margin changes at the plant level within the flex simulation. Of interest are the changes over time when either sugarcane or maize serves as the feedstock. The actual sugarcane end-of-season dates serve as the start date for maize-based production. Actual start dates of the sugarcane season signal the end of the maize period. In this way we explore the financial implications of becoming a flex mill; employing two sources of feedstock instead of one.

Historical prices for ethanol and maize are weekly. Sugarcane prices are monthly and derived from UDOP (2007). The Consecana Council that represents farmers, manager mills and buyers in Sao Paulo State sets the price to be paid by the industry. Mato Grosso does not have such a council setting price. Each mill in Mato Grosso sets a monthly price to be paid per ton of raw-material using the Consecana reference price set in the eastern part of the country. Mills though own most of their own land making raw material acquisition cheaper. For this study the companies have not provided historical cost data of the sugarcane they process. So the prices defined for Consecana Council are used to simulate price behavior in Mato Grosso.

Direct interviews with the managers of the eleven mills in the state yielded the costs of ethanol production (excluding sugarcane), volume of ethanol produced and quantity of sugarcane crushed for the 2004/05, 2005/06 and 2006/07 crop years. Those data are not publically available and we are indebted to the firms for their cooperation. The eleven mills in Mato Grosso produced ethanol between 126 and 223 days a year from sugarcane during the last half of the calendar year. Maize prices in Mato Grosso vary regionally. Prices for maize rise moving from North to South. Price is influenced by distance to internal and external markets and the fact that the largest supplies originate in the North. Transportation is only by truck and road infrastructure is poor (Figure 3). The simulation uses the geographically closest reference price as the case study mill’s cost of maize; Diamantino, Mato Grosso. The simulation of maize introduction occurs at the time each of the eleven plants ceased (sugarcane-based) production during the study period.

Maize price analysis shows a sharp rise following the increase in crude oil prices during the second semester of 2007. Prices reached $4.90 per bushel in Mato Grosso, while at the same time a bushel of maize in Sao Paulo State was worth $8.00. Weekly historical ethanol price data are the third and final price series used in the simulation. These were obtained from Sindalcool (Alcohol and Sugar Industries Syndicate of Mato Grosso), and represent the wholesale price received by the mill. Anhydrous and hydrated1 prices have risen over time, but not as dramatically as seen in the maize and sugarcane series. The highest market prices during the period (about R$1.02 per liter for anhydrous and R$0.84/L for hydrated ethanol occur between December and April (Figure 4). This is expected as supplies fall due to mill closure because of the completion of the sugarcane harvest. There is less than 20% annual ethanol storage in the state.

1 Hydrated ethanol is the raw product from the ethanol process and contains small quantities of water. Anhydrous alcohol is 100% pure.



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5. Sugarcane Mill Descriptive Statistics

Seasonal operating and idle periods are known for the case study plant from 2004

through 2007, as are annual anhydrous and hydrated ethanol production, and sugarcane purchases. Corresponding yields of ethanol per ton of sugarcane were calculated (Table 1). Maize utilization and ethanol production are assumed to be uniform, reflecting the storability of dried grain as a feedstock. The ratio of anhydrous to hydrated ethanol produced is assumed to be similar whether the feedstock is sugarcane or maize.

Feedstock throughput is a critical variable in determining plant operating efficiency and capital utilization. Feedstock throughput varies significantly between sugarcane and maize (Table 2). While sugarcane contains more potential energy per hectare than maize, maize is more gravimetrically dense because it is dry when processed. Thus maize contains more energy per ton. An ethanol plant yields more ethanol per ton of feedstock when the material is maize when only considering gravimetric density. But more importantly, maize moves much more slowly through a plant than sugarcane. The temporal density of sugarcane overshadows the relatively higher gravimetric density of maize, and thereby reducing plant capacity under a maize configuration. Slower fermentation requires higher fermentation storage capacity investment in an attempt to maintain throughput. This raises capital costs and lowers capital use efficiency, when compared to a sugarcane configuration.

Plant throughput is defined as the yield of ethanol per ton of feedstock and per unit of time. High throughput is essential for efficient asset utilization. For example, assume the fermentation time necessary for maize as raw-material is five times longer than for sugarcane and a ton of corn produces four times more ethanol than a ton of sugarcane. The throughput ratio for maize would be 0.8. Introducing maize to sugarcane plant yields 80% of the ethanol per unit of time. The throughput ratio between maize and sugarcane would be equal if the ethanol yield from maize was still four times the sugarcane yield, but maize fermentation time could be improved 20% due to enhanced microbial activity. Conceptually then maize-based can yield similar throughputs to sugarcane, but in current practice2 maize throughput and capital use efficiency are lower.

Maize’s slow sugar conversion requires more physical capital, ceteris paribus, to increase capacity in order to achieve reasonable throughput. The manager solves a capital utilization calculus balancing increased capital costs with improved throughput. In future work we solve this calculus for the mills in Mato Grosso. Higher capital levels reduce capital use efficiency and asset turns as more capital is needed to produce the same quantity of ethanol per unit of time when compared with a sugarcane feedstock model.

Plant location and firm size affect gross margin under a flex configuration. Raw material prices and transport distances factor into the optimal location decision. Larger firms have longer sugarcane and shorter maize seasons. On average, the largest mills operate for 35 weeks of production and the smallest companies for 23 weeks. The largest companies produce ethanol at least three weeks before the smaller mills and they close for

2 Recent research indicates new enzymes will dramatically increase maize throughput (Singh, 2008)



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the season four weeks later than the smaller mills. Large plant managers attribute the longer operating window to better fertilizer management and the use of growth regulators that sustain feedstock quality and quantity over longer periods (Personal Communication, 2007). Not surprising longer sugarcane processing seasons are also correlated with higher yields of ethanol per ton of sugar cane.

Weekly gross margin for the case study mill is a function of the price of ethanol, the price of feedstock (maize or sugarcane) and production yield. Changing the production yield parameter changes the gross margin and throughput. Feed stock purchase and ethanol sale are assumed to occur simultaneously within the same week. This assumption ignores sugar cane mills forward contracting for raw material. The assumption though may be reasonable for sugarcane and unreasonable for maize. Using current price reflects the value of sugarcane at the time of harvest and use for integrated mills. As noted above the mills of Mato Grosso produce 70% of their sugarcane and sugarcane is a non storable raw material.

The assumption of weekly purchases of maize is less reasonable as a part of the maize-for-feedstock season occurs prior to planting when maize supplies are at their lowest. Maize planting occurs in March following soybeans, with harvest occurring in June. Sugarcane supplies would still be ample during the maize harvest window. Maize though is a highly storable and transportable commodity, and there is little farmer storage in Mato Grosso. Purchasing maize at harvest when prices are low and then storing the grain until needed by the flex mill would be the likely strategy.

The case study mill is located in west central Mato Grosso and is one of the larger producers in the state. On average over the study period the mill operates 59% of the year, utilizes almost nine thousand tons of sugarcane per day and produces 745 thousand liters of ethanol on each of the 196 days of the season. The mill produces both anhydrous (46%) and hydrated (54%) alcohol. We use the actual days of operation during the 2004-2007 study period when sugarcane was used as the raw material. We add a plant maintenance period of 35 days for the case study mill and assume a maize throughput of .53 the level of sugarcane. That throughput assumption is based on: 1) a maize:sugarcane yield ratio of 4.443; and 2) a maize sugarcane fermentation ratio of 8.464.

6. Results and Discussion

The issue of whether to introduce maize into a sugarcane mill hinges on several key factors. The first set of factors relates the tradeoff between maize’s superior gravimetric density with its poor temporal density, when compared to sugarcane. To better understand this aspect of the problem, assume that ethanol yield is 90 liters per ton of sugarcane. A mill would produce about 785,970 liters of ethanol (using the average daily demand for sugarcane of 8,733 in our study mill) (column 2 of Table 3). A maize based mill only produces 52% of that amount, or 410,965 liters on the same mass of feedstock, even though the yield is 400 liters per ton. Slow fermentation time (8.5 times slower) represents

3 400 liters per ton of maize/90 liters per ton of sugarcane 4 55 hours for maize/6.5 hours for maize



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the larger challenge for bioenergy systems, where flexibility of feedstock inputs often lowers cost per feedstock unit, but disrupts processing efficiency. Slower throughput reduces asset turns lowers returns on capital and. Throughput falls in our example from 786 thousand liters per day to 411, and as a result may not provide the sufficient cash flow to service overhead and fixed cost obligations. This especially may be problematic if more capital is required to adapt a plant to receive maize.

At issue is how to cost effectively move to left in Table 3. The highlighted yellow region contains all the linear combinations between yield and fermentation time that yield greater or equal outputs from the same quantity of biomass. Moving to this superior region involves either investing in yield improving technologies that increase ethanol yield from maize above 400 liters per ton, or technologies that reduce the fermentation time. Any benefits to reducing fermentation time may be swamped by increased costs (fixed and/or variable) associated with expanding capacity. Critical for efficient maize utilization therefore depends on effectively managing fixed costs associated with maize as a feedstock. Fermentation time needs to be reduced maize to sugarcane ratio of 4:1 from a 8.5:1, at minimum or plant capacity will be reduced. Ceteris paribus, driving down maize fermentation time increases mill capacity and output, cash flow, asset turns, capital utilization, and maize demand (Figure 5).

Not discussed in this article is the technology and capital investment required to introduce maize into a sugarcane mill. There are three central cost elements for adapting a mill to handle maize. The first are the relatively small costs of receiving, handling, storing, and preprocessing maize. The technology is known and equipment widely available. The second costs are for expansion of the plant’s fermentation capacity to speed up the throughput when maize is employed as the feedstock. The cost of expanding fermentation tank capacity may not be justified as returns to capital will be low. Finally, is the introduction of novel enzymes to improve yield and reduce fermentation time. These variable use technologies may prove promising as mill capacity is improved without the expansion of the physical plant.

The crush margin, the revenue per liter of fuel (ethanol) minus the feedstock cost, reflects the economic viability of the bioenergy production model. Flex mills complicate this common metric because as discussed above a ton of sugarcane and a ton of maize differ on throughput. Nonetheless generally ethanol prices are higher during the off-season when maize would be employed. One advantage of the Flex concept is to operate plants when supplies are reduced and prices tend to be higher.

Maize prices had a maximum price of $R200 and a minimum of $R130 per ton of maize, while sugarcane ranged between $R25 and $R50 per ton during our study period. Maize had a both a slightly higher maximum and a higher minimum price when measured on a sugarcane equivalent basis, but showed less volatility (Table 4). The two price series appear to be negatively correlated during the brief study period, and are analyzed in more in depth in a separate article. Anhydrous and hydrated ethanol prices showed similar levels of volatility, around 100% of the period.

Due to pricing data availability the Flex simulation begins the week of July 31, 2004 when the case study mill had already begun sugarcane processing/ethanol production season. Sugarcane processing occurs over four separate occasions and maize three times



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over the 153 weeks of the simulation. The mill shuts down for maintenance for 35 days for three separate periods. The simulation concludes the week of June 30th, 2007.

Weekly gross revenue, as measured by the quantity of anhydrous and hydrated ethanol produced multiplied by the weekly price in Mato Grosso, peaked at $R5.9 million in early May 2006 when sugarcane was the feedstock, and peaked at $R3.4 million early March of 2006 when maize was the feedstock (Figures 5) . Weekly mill income averaged $R4.2 million when the feedstock was sugarcane and $R2.5 million or 40% less (Table 4).

Average weekly gross margins were about 23% less during the maize periods as compared to the periods when sugarcane fed the plant (Figure 6 & 7). The minimum gross margin of $R673 thousand occurred the week of September 30th, 2006 when the feedstock was sugarcane, and $R520 thousand for maize. That September period was the only week when gross margins fell below 20% for either sugarcane or maize. Gross margins averaged 56% for maize and only 45% for sugarcane. It should be restated that while maize prices are local, sugarcane prices originate from Sao Paulo because local prices do not exist. It is likely that sugarcane prices in Mato Grosso are lower than in Sao Paulo.

Feedstock as a percentage of gross revenue (inverse of the “crush margin”) is lower on average during the study period for sugarcane, compared to maize (Figure 8). In late 2006 margins shrank significantly for sugarcane based hydrated ethanol when the cost of raw material surpassed 90% of revenue (Figure 9). The lowest cost of materials relative to revenue occurred in the off season of early 2006 when maize used as the feedstock for anhydrous ethanol comprised only 30% of revenue.

Feedstock on average cost 50% less per week under a maize configuration (Figure 10). This is due to the fact that simple direct substitution of sugarcane with maize drastically reduces the throughput of a sugarcane mill, and as a result feedstock utilization. As noted above though throughput slows gross margins are reduced to a lesser degree. The resiliency of gross margins under the maize configuration is due to the average ethanol prices being higher during the maize period, 9% and 22% respectively for anhydrous and hydrated. The case study mill as well produced 17% more hydrated than anhydrous ethanol. The relative cost of the feedstock was not a factor as the cost of maize was only 3% higher on average over the study period. As noted above the cost of sugarcane used in the study is high and the differences between the two feedstock costs probably are greater. In separate research we address the statistical relationship between the two price series.

7. Conclusion

The working hypothesis underlying our work is that bioenergy models employ capital poorly because of its weak density properties. This research looks at a small piece of this question by presenting a case study of extending the operating season of a sugarcane mill. The study lays out some key issues that require deeper understanding.

First is the importance full year operation. Full year feedstock production, low cost storage and transport improve capital use efficiency. The large capital required to build and operate an industrial bioenergy processing facility benefits from year around operation to exploit economies of scale, assure efficient use of capital, and allow for significant investments in technology. Seasonal feed stock production or feedstock procurement that



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can be subject to an economic holdup are ill suited for maximizing the efficiency of a capital intense processing facility.

Second, multiple feedstocks (“multi-ins”) permits portfolio pricing of feedstock, avoids shortages and holdups, and allows for risk management. In our case study 70% of the feedstock was owned by the mill. Commodity feedstock purchasing, as opposed to proprietary crop production (the case in Mato Grosso), allows the mill greater flexibility and superior risk management. Vertical integration guarantees a supply but restricts the mill’s participation in spot markets where prices may be lower and risk management more effective. There were occasions during the study period when gross margins were higher had the mill utilized maize, even when sugarcane supplies were plentiful. At the same time, on average sugarcane was the lower cost feedstock. The key is flexibility.

Third is the issue of switching costs. The fixed and variable costs for adapting a mill to handle multiple feedstocks need to remain low in bioenergy plants because of the low energy densities. The case study mill would have over $R200 thousand less per day in gross margins had the mill remained idle during the 134 day off-season. Nonetheless operating a mill in the off season, adapting the plant to receive and handle maize, and investing additional capital to convert the maize into sugarcane may not be profitable, though gross margins are positive. In a separate research paper we estimate and factor in those costs.

This study looked simply at introducing a second feedstock during a period when the mill was ordinarily idle. A second order of flexibility appears to be the capability to not use maize if prices became unfavorable. True flexibility would imply that operating and capital use efficiencies were such that when relative prices rise in general or for a particular feedstock in particular, the mill has the ability to close or switch sources. A third degree of flexibility would be for mill operators in Mato Grosso to not produce and own their own feedstock, but be able to purchase commodities, and waste, co, and by- products when price and availability are most advantageous. There has been a similar switch in feedstock model for biodiesel plants in the United States. Investment activity has shifted to multiproduct plants, when early on most plants were fueled by a single feedstock, soybean oil. A tripling of food oil prices in 2007 made investors acutely aware of the value of multiple sources of bioenergy feedstocks, especially by-product and waste.

Figure 1. Mato Grosso, Brazil - Agricultural Calendar.



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Crop

1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21 1st 11 21

COTTON H H H H H S S S S S S S S S S H H H H H H H H

RICE S S S S S S S S S S H H H H H H H H H H H H H H

CORN S S S S S S S S S S H H H H H H H H H H H H H H H H

2nd CORN S S S S H H H H

SOYBEAN S S S S S S S S S S H H H H H H H H H H H

SUGARCANE H H H H H H H H H H H H H H I I I I I I I I I I I I I I H H H H H H H H

springwinter

MAR APR MAY JUN

summer fall

SEP OCT NOV DEC JAN FEBJUL AUG

H = Harvest. S = Seeding. I = Sugarcane’s Inter-harvest. Note that maize is planted after the soybean harvest in February. Own-elaboration. Source: Zoneamento Agrícola para a cultura de milho 2ª safra no Estado de Mato Grosso, ano-safra 2008/2009. Portaria Numero 260., 2009; Zoneamento Agrícola para a cultura de milho no Estado do Mato Grosso, ano-safra 2009/2010. Portaria Numero 91., 2009; Zoneamento Agrícola para a cultura de soja no Estado de Mato Grosso, ano-safra 2009/2010. Portaria Numero 185., 2009

Figure 2. Maize Prices in Mato Grosso State: 2004-2008

Source: Confederação da Agricultura e Pecuária do Brasil, 2007

Figure 3. Cost of Feedstock: Maize, Sugarcane, and Maize on a sugarcane equivalent basis



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Source: www.udop.com.br; and authors’ calculations



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Figure 4. Anhydrous and Hydrated Ethanol Prices in Mato Grosso: 2004-2007

Source: Sindalcool Syndicate (2007)

Figure 5: The Impact of Reducing Maize Fermentation Time for a Fixed Capacity Flex Mill

Current Maize: Sugarcane Ratio is 8.46:1



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Figure 6. Weekly Gross Revenue Simulation over the 153 Week Study Period for a Flex-Mill in Mato Grosso

Figure 7. Weekly Gross Margin Simulation over the 153 Week Study Period for a Flex-Mill in Mato Grosso



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Figure 8. Weekly Gross Margin Percent over the 153 Week Study Period for a Flex-Mill in Mato Grosso

Figure 9. Feedstock as a Percentage of Ethanol Value



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Figure 10. Weekly Cost of Feedstock as a Percentage of Revenue for a Flex-Mill in Mato Grosso

Figure 11. Weekly Cost of Feedstock Simulation over the 153 Week Study Period for a Flex-Mill in Mato Grosso



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Table 1. Descriptive Statistics: Case Study Sugar Cane Mill in Mato Grosso Sugarcane Season Sugarcane Ethanol

Days Percentage Demand Annual Production (1,000 liters) Daily Production (1,000 liters) Start End On Off On Off Tons tons/day Anhydrous Hydrous Total Anhydrous Hydrous Total 4/22/2003 11/7/2003 200 130 61% 39% 1,388,540 6,943 54,381 63,839 118,220 272 319 591 5/5/2004 11/23/2004 203 127 62% 38% 1,758,805 8,664 69,562 81,660 151,222 343 402 745 5/5/2005 10/11/2005 160 170 48% 52% 1,615,463 10,097 64,116 75,266 139,382 401 470 871 5/3/2006 11/25/2006 207 123 63% 37% 1,810,371 8,746 69,928 82,089 152,017 338 397 734

4/16/2007 11/11/2007 209 121 63% 37% 1,926,239 9,216 75,440 88,560 164,000 361 424 785 Average 196 134 59% 41% 1,699,884 8,733 66,685 78,283 144,968 343 402 745

Table 2: Flex Mill Throughput Scenarios When Maize is the Feedstock

Yield (L/ton) Fermentation (hours)

Sugarcane Corn Yc/Ym Sugarcane Corn Fc/Fm

65 360 5.54 6.5 35 5.38 65 400 6.15 6.5 45 6.92 65 420 6.46 6.5 55 8.46 65 450 6.92 6.5 65 10.00 70 380 5.43 6.5 72 11.08

70 400 5.71

70 500 7.14

75 380 5.07

75 500 6.67

80 360 4.50

80 420 5.25

80 450 5.63

85 400 4.71

85 420 4.94

85 450 5.29

90 360 4.00

90 380 4.22

90 400 4.44

90 420 4.67

90 450 5.00

90 500 5.56 95 420 4.42

95 450 4.74

100 380 3.80

100 420 4.20

100 450 4.50

105 360 3.43

105 400 3.81

105 500 4.76

110 360 3.27

110 420 3.82



18

Table 3. Daily Ethanol Output under a Sugarcane or Maize Configuration

Notes: Yield = liters per ton; fermentation time relative to sugarcane; processing capacity assumed fixed to that of the case mill

Table 4. Simulation Descriptive Statistics: Case Study Mill in Mato Grosso Brazil -

Ethanol (Anhydrous and Hydrated) – In R$ (Brazilian Reais) Revenue Gross Margin % Revenue Cost of

Feedstock % of Revenue

Stats Sugarcane Maize Sugarcane Maize Sugarcane Maize Sugarcane Maize Sugarcane Maize Average 4,162,050 2,481,590 1,888,492 1,451,183 45% 56% 2,273,558 1,030,406 55% 44% Maximum 5,894,430 3,375,815 3,147,421 2,323,915 64% 71% 2,898,168 1,310,442 81% 72% Minimum 2,868,144 1,830,536 672,531 520,094 19% 28% 1,568,369 749,592 36% 29% Standard Deviation 566,203 486,141 657,549 568,356 13% 14% 464,892 171,057 13% 14%

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