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Quick-fiber process: Energy, water, and economics
Industrial Crops and Products 34 (2011) 986–993
Contents lists available at ScienceDirect
Industrial Crops and Products journal homepage: www.elsevier.com/locate/indcrop
Process modeling of the Quick-germ/Quick-fiber process: Energy, water, and economics Tao Lin a,1 , Luis F. Rodríguez b,∗ , Steven R. Eckhoff c,2 a b c
University of Illinois at Urbana-Champaign, 374 Agricultural Engineering Sciences Building, MC-644, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, United States University of Illinois at Urbana-Champaign, 376C Agricultural Engineering Sciences Building, MC-644, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, United States University of Illinois at Urbana-Champaign, 360C Agricultural Engineering Sciences Building, MC-644, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, United States
a r t i c l e
i n f o
Article history: Received 21 December 2010 Received in revised form 1 March 2011 Accepted 3 March 2011 Available online 6 May 2011 Keywords: Process simulation model Quick-germ/Quick-fiber process Water balance Energy balance Economic Bioenergy
a b s t r a c t A process simulation model was developed on the SuperPro Designer® platform for a Quick-germ/Quickfiber (QQ) modified dry grind ethanol fractionation facility. This paper compares energy and water demands, product quality, and economic performance between the QQ and the dry grind ethanol processes. Results showed that the QQ process reduces energy demand by 31.6% and water demand by 17.9%, produces more value-added coproducts, and improves the economic viability. The QQ process has a lower ethanol yield (0.405 vs. 0.414 L of ethanol per kg of corn) because of starch loss at the front-end separation. This model can be used to provide decision support for ethanol producers considering the new emerging technology. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Currently, corn is the primary feedstock source providing ethanol in the US; however, it has been widely debated whether ethanol produced from corn is sustainable in the long term (Farrell et al., 2006; Pimentel et al., 2007). Environmentally, the major concern is that producing ethanol from corn demands intensive water and energy consumption. Economically, recent fluctuations in petroleum, ethanol, and corn prices have driven several large producers of ethanol into bankruptcy or acquisition.3 The ethanol industry is vulnerable to periods of economic weakness because its product value varies with oil prices but its raw material (corn) cost varies with food prices. When ethanol prices are strong, dry grind processors focus on how to improve ethanol yield, because coproduct values are not as significant (Rodríguez et al., 2010). However, when corn prices are high and ethanol prices are low, the dry grind processors lose money rapidly (Tiffany and Eidman,
∗ Corresponding author. Tel.: +1 217 333 2694; fax: +1 217 244 0323. E-mail addresses: [email protected] (T. Lin), [email protected] (L.F. Rodríguez), [email protected] (S.R. Eckhoff). 1 Tel.: +1 217 333 2694; fax: +1 217 244 0323. 2 Tel.: +1 217 244 4022, fax: +1 217 244 0323. 3 Wall Street Journal. 2009. VeraSun Seeks Bankruptcy Protection. Available at: http://online.wsj.com/article/SB122552670080390765.html. Accessed 20 December 2009. 0926-6690/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.indcrop.2011.03.003
2003; Rodríguez et al., 2010). Wet milling is a more stable method for ethanol production because its coproduct values usually follow corn price trends. When corn prices are high, coproduct value increases to offset the lower ethanol prices. To stabilize ethanol production, dry grind ethanol processors need to develop their coproducts in order to get through the times when ethanol prices are low and corn prices are high. In response to this need, several modified dry grind processes have been developed to improve the profitability of ethanol production; significant improvements have been observed on the processing efficiency and the nutritional characteristics of coproducts at the laboratory scale (Singh et al., 2005). The Quickgerm/Quick-fiber (QQ) process (Singh and Eckhoff, 1996; Singh et al., 1999) is one of these modified dry grind processes. It adapts a part of the wet milling process (the germ and fiber recovery system) for the dry grind process. This process has three key advantages: recovered fiber and germ can be further processed to generate value-added coproducts such as germ oil, corn fiber oil, and corn fiber gum; removal of germ and fiber can increase the protein content of distiller’s dried grains with solubles (DDGS), making this coproduct suitable as a feed for non-ruminant animals such as swine and poultry; separating the nonfermentable fractions before fermentation will improve the process efficiency by 14% (Singh et al., 1999, 2005). Recently, Li et al. (2010) and Rodríguez et al. (2010) developed a detailed engineering economic spreadsheet model that showed that the QQ process improves
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Clean Corn Soaking Vent Grinding Germ and Fiber
Germ & Fiber Separator
Dryer
Liquefaction
Jet Cook
Cooler
Saccharification CO2 Scrubber
Fermentation
CO2
Fresh Water Distillation
Dehydration
Whole Stillage
Thin Stillage
Ethanol
Centrifugation Wet Cake
Separator Slurry
Evaporation
Liquid Gas
Vent
Dryer
DDGS
Fig. 1. A simplified flow sheet of the QQ process.
the economic viability of the dry grind process at the commercial scale. Although these previous studies showed the QQ process is economically viable, there is no analysis that details the energy and water consumption of the QQ process and compares the process performance to the dry grind process. The objective of this study was to develop a simulation model of the QQ process on the SuperPro Designer® platform to predict operating performance at the commercial scale using a continuous process and compare it to the USDA’s dry grind model (Kwiatkowski et al., 2006). This study identifies and compares the energy consumption, water usage, product compositions, and economic performance of the QQ and the dry grind processes.
2. Methods 2.1. Process model description The QQ process model developed in this study is based on the USDA’s dry grind (Kwiatkowski et al., 2006) and wet milling models (Ramirez et al., 2008); the previous dry grind and wet milling models are developed on the SuperPro Designer platform as is the model developed here. This work is augmented by the previous work of the investigators (Li et al., 2010; Rodríguez et al., 2010). The dry
grind model developed by USDA is designed to process 1.12 million kg of corn per day (43,800 bu/day) (Kwiatkowski et al., 2006). Because of the separation of corn germ and fiber at the front end, a higher rate of corn input can be processed with the same size of equipment used in the dry grind process (Singh et al., 2005). By adding a germ and fiber separation system to the dry grind process, a QQ process model is developed to process 1.34 million kg of corn per day (52,500 bu/day), without increasing the size of the equipment used in the dry grind process designed by Kwiatkowski et al. (2006). Both plants operate 24 h per day and 350 days per year, with time set aside for maintenance and repairs. The QQ process consists of six major sub-systems: grain cleaning, germ and fiber recovery, liquefaction and saccharification, fermentation, distillation, and DDGS recovery. A simplified flow sheet is provided in Fig. 1. Detailed information regarding process requirements are available in Kwiatkowski et al. (2006); only significant changes specific to the QQ process model are detailed here: the consideration of corn composition as impacted by the germ and fiber recovery sub-system, and the fermentation sub-system.
2.1.1. Corn composition Assuming 14.5% moisture content, 1 bushel, or 25.5 kg, of corn will have 21.8 kg (48.0 lb) of dry materials (Watson, 1987). Corn kernels can be divided into three major parts: pericarp and tip cap;
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T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993 Washing Water
Germ and Fiber Stream 1st Screen
Mixer
Dewatering Screw 2nd Screen
Blending Tank
3rd Screen Send to the fluidized bed dryer
Process Water
Corn Mixer
Corn Slurry Soaking Tank 1st Grinding
1st Grind Tank
Hydrocyclone 1 Corn slurry after separation Hydrocyclone 2 2nd Grind
Send to fermentation
Fig. 2. A detailed view of the germ and fiber recovery sub-system.
endosperm; and germ. Starch, oil, neutral detergent fiber (NDF), protein, sugars, and ash are the major constituents making up the corn kernel and are each distributed throughout the three parts. To better describe the coproducts recovered via the QQ process, the protein and NDF are further divided based on solubility and their location within each part of the corn kernel. Detailed information regarding the corn composition is provided in Table S1 in the Supplementary Material on the Web. 2.1.2. Germ and fiber recovery sub-system The germ and fiber recovery sub-system, shown in Fig. 2, involves the soaking of the corn kernel for 8 h, a two-stage grind, germ and fiber recovery using hydrocyclones, three-stages of germ and fiber washing, and germ and fiber separation and dewatering. The moisture content of corn increases from 14.5 to 57.4% during soaking. The separation system is based on the density difference between the constituent parts of corn. It consists of two stages of hydrocyclones and three stages of wash screens. The diluted slurry from the mill is pumped to hydrocyclones, where the lighter parts, germ and fiber, are floated off the top. The washing system consists of three gravity screens used to wash the loose starch and protein from the germ and fiber. Water is recycled from the last two stages of washing. The wash water finally leaves in the underflow of the first screen with the free starch and protein. The detailed operational information of the equipment, such as top flow ratio and output dry mass, is provided in Table S2 in the Supplementary Material on the Web. Based on the ratio set in this model, approximately 95% of pericarp fiber and 90% of germ are separated from the corn slurry. After washing, the germ and fiber are then dewatered in the screw press to an average of 50% moisture content. Germ and fiber are further dried and separated via a fluidized bed dryer (not
pictured) to 3% and 10% moisture content, respectively. The outlet stream of germ and fiber are produced at a rate of 4228 and 3871 kg/h, respectively. 2.1.3. Fermentation sub-system Fermentation is a conversion step where glucose is converted into ethanol and carbon dioxide with yeast. Urea is added at the rate of 0.3% (wet basis) of corn input to provide a nitrogen source for the yeast propagation. The fermentation simulated in this process is continuous, and the residence time is set at 60 h. In this model, 91.9% of total glucose is assumed to be converted to ethanol and carbon dioxide, on a mass basis: C6 H12 O6 → 0.511 C2 H5 OH + 0.489 CO2 .
(1)
In addition to the glucose consumed in Eq. (1), 3.28% of the initial glucose is consumed for the yeast propagation, and carbon dioxide is produced via yeast metabolism: 0.88C6 H12 O6 + 0.12(NH2 )2 CO → 0.742YeastDryMatter + 0.258CO2 .
(2)
The remaining 4.82% of the glucose is not involved in the conversion and remains in the DDGS. The extent of conversation is based on industrial data and research data. 2.2. Cost model description A cost model was developed on the SuperPro Designer® platform to evaluate the economic performance of the QQ process. Capital investment costs, operating costs, and product values are vital for the economic analysis.
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Table 1 Total capital investment costs for the QQ process. Factor of purchased equipment costa
Cost (Million $)
Cost ($/L)
Cost ($/gal)
Purchased equipment Purchased equipment installation Piping Instrumentation and control Electrical equipment and material Building Yard improvement Total direct costs
100% 40% 30% 10% 15% 40% 15% 250%
24.13b 9.65 7.24 2.41 3.62 9.65 3.62 60.33
0.12 0.05 0.04 0.01 0.02 0.05 0.02 0.31
0.47 0.19 0.14 0.05 0.07 0.19 0.07 1.18
Engineering Construction Total indirect costs
25% 45% 70%
6.03 10.86 16.89
0.03 0.06 0.09
0.12 0.21 0.33
Fixed capital costs Working capital costs Startup capital costs Total capital investment costs
320% 48% 32% 400%
77.22 11.24 7.72 96.18
0.40 0.06 0.04 0.50
1.51 0.22 0.15 1.88
a b
Douglas (1988). The purchased equipment cost is estimated by the QQ process model developed here.
2.2.1. Capital investment costs Capital investment costs are estimated based on the purchased costs of each piece of operating equipment. Based on the cost estimation factors developed by Douglas (1988), the total capital investment cost would be four times the total equipment purchased costs (Table 1). The equipment costs are based on the SuperPro Designer® equipment cost estimating parameters as well as USDA’s dry grind and wet milling models (Kwiatkowski et al., 2006; Ramirez et al., 2008), and the equipment costs are adjusted for capacity using standard engineering scaling factors. 2.2.2. Operating costs Operating costs of an ethanol plant consist of raw materials, utility, labor, and facility dependent costs. Based on the processing rate, the amount of raw materials and utilities required by the plant can be calculated by the model simulation. The unit costs of the raw materials and utilities are the input values, shown in Table 2. These costs are based on the market prices observed in April 2009 as well as the literature (Kwiatkowski et al., 2006; Li et al., 2010). Facility dependent costs include maintenance costs, insurance, local taxes, and other factory expenses. Maintenance costs are assumed to be 3% of direct fixed capital costs, while insurance and other factory expenses are at 0.85% and 0.7% of direct fixed capital costs, respectively (Kwiatkowski et al., 2006). Depreciation and amortized loan payments are generally associated with the facility dependent costs. The QQ plant considered here is assumed to be financed without any external loans, and thus no amortized loan payment is needed. The plant has a 15-year lifetime with zero salvage value at the end. The annual depreciation cost is calculated via the straight-line method.
Eight operators and one supervisor are assumed to work full time in this QQ ethanol facility, and five operators are assumed to work in the dry grind plant (Kwiatkowski et al., 2006). The unit labor costs of an operator and a supervisor are at an all-inclusive rate of $69/h and $105/h, respectively. 2.2.3. Products values In the QQ process, ethanol, DDGS, germ, and fiber are the four products marketed, whereas CO2 , a potentially marketable product, is assumed to be purged to atmosphere. The prices of ethanol, DDGS, and corn oil in this analysis are based on the market prices as observed in April 2009 and are shown in Table 2. The value of the fiber fraction is set at $0.08 per kg based on its fraction within the DDGS and DDGS prices. The value of the germ fraction is set at $0.37 per kg, based on the corn oil and protein values and oil extraction costs (A detailed germ value calculation is provided in the Supplemental Materials). 2.3. Energy demand analysis The stream data related to the heating demand includes the specific heat capacity of raw materials, temperature, and flowrate for each process stream, as well as the residence time of each unit operation. With the aid of simulation software (SuperPro Designer® ), the model calculates the specific heat of each stream based on its composition. Corn is the single major dry matter input in the process. To facilitate the comparison of two dry grind models consistently, the specific heat capacity of each corn component in the QQ model is based on the assumptions used in the USDA’s dry grind model (Kwiatkowski et al., 2006). Under these assumptions, the QQ
Table 2 Unit operating cost and product value of raw materials, utilities, and products in the QQ process. Unit Cost Raw materials Corn Alpha amylase Glucoamylase Yeast Urea Succinic acid Process water Denaturant
$4.2/Bua $2.25/kgb $2.25/kgb $1.86/kgb $0.31/kgc $0.11/kgb $0.35/kLb $0.475/kgc
Unit cost Utilities Natural gas Steam Electricity Cooling Water
$4 per MMBtuc $5 per MMBtud $0.078/kWhc $0.35/kLb
Unit value Products Ethanol DDGS Germ Fiber
$1.72/gala $130/tona $0.37/kg $0.08/kgc
a Market prices as observed in April 2009: corn price: http://www.ams.usda.gov/mnreports/gx gr115.txt (accessed on May 14, 2009); DDGS price: http://www.ams.usda.gov/mnreports/sj gr225.txt (accessed on May 14, 2009); ethanol price: http://www.ethanolmarket.com/fuelethanol.html (accessed on May 14, 2009). b Kwiatkowski et al. (2006). c Li et al. (2010). d Purchased at 1.25 times the natural gas cost.
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process model quantifies the heating duty of each unit operation based on its operational requirements. The operational parameters, such as temperature, moisture content and residence time, are input values and can be easily modified to suit user preferences. The temperature and moisture content of several major streams in the QQ process are provided in Table S3 in the Supplementary Material on the Web. Steam and cooling water are used to adjust working temperature of each unit operation, while natural gas is exclusively used for the dryer unit operation. 2.4. Water demand analysis The water demand of corn-to-ethanol production can be composed of two parts: process water and non-process water demand. The process water is the water directly mixed with ground corn to form the slurry. For the process water demand analysis, based on the feedstock input rate and the moisture content requirement of each processing stream, the simulation model would quantify the process water input rate. Cooling water and steam water are two types of non-process water in ethanol production. As steam is purchased as a utility, not generated at the site in this model, steam water is not considered in this analysis. Based on the previous study, steam water only accounts for less than 3 percent of total water demand (Wu et al., 2009). Therefore, the exclusion of steam water demand would not have a major impact on the total water demand analysis. The cooling water considered here is a recirculating noncontact system, where the cooling water losses mainly occur in the cooling tower via evaporation, blowdown, and drift. In a welloperated recirculating cooling system, blowdown and drift water loss account for only a small portion of cooling water usage (Asano et al., 2007). To simplify the analysis, the cooling water demand is based on the cooling water evaporation rate. Based on the cooling requirement in each unit operation, the simulation model quantifies the circulation flowrate of the cooling water. In the QQ process model, the supply and return temperature of cooling water are designed at 15 and 26 ◦ C, respectively. Based on a simplified mass and energy equation (Asano et al., 2007), 2300Qe = 4.2Qc T
(3) rate.4
the evaporation rate would be 2% of the circulation Qe is the evaporation rate (kg/h); Qc is the circulation rate (kg/h); T is the temperature change (◦ C). Without considering the blowdown and drift loss in this analysis, the makeup cooling water usage consists of 2% of the circulation flowrate. 2.5. Economic analysis A baseline scenario is selected based on the input information described in the cost model description section. No external loans or dividend is assumed for the baseline analysis. Therefore, the annual cash flow is determined by the sum of the income after tax as well as depreciation. The analysis does not consider the impacts of inflation during the lifetime of the facility, thus it would be considered in real terms. The payback period for the QQ facility is determined as a ratio of total capital investment cost to annual cash flow. Two additional economic scenarios are considered as well. First, an ownership group is presumed to provide 100% of the capital requirement. The investors are presumed to collect dividends rated at 8% of the original equity invested. Dividends are subtracted from the cash flow calculation described above. Second, building from
4 Note: 2300 kJ is required to evaporate 1 kg of water, while 4.2 kJ is required to cool 1 kg of water by 1 ◦ C.
Fig. 3. A comparison of the energy demand in the QQ and dry grind processes determined here, as compared to previous industrial surveys: USDA 2002 survey: Shapouri and Gallagher (2005); RFA 2007 survey: Wu (2008).
the investor scenario, those investors are presumed to provide 40% of the initial capital, thus requiring the remaining funds to be acquired via loan. The interest rate of the load is assumed to be 10%, with payments amortized over the period of 15 years. Amortized loan payments are presumed to be operating costs for the facility, thus affecting net profit. The payback period is determined as a ratio of the equity by the investors to annual cash flow. 3. Results and discussion 3.1. Energy demand Based on the QQ simulation model, the QQ process utilizes 8.7 MJ of energy to produce each liter of ethanol (31,342 BTU/gal) (Fig. 3). Steam is the largest required energy resource in the process, accounting for more than half of the total energy demand. A significant amount of steam is used to provide heat in the distillation and liquefaction unit operations. Natural gas is the second largest energy source used to dry the coproducts including germ, fiber, and DDGS, which accounts for approximately 3.6 MJ/L (12,880 BTU/gal). Approximately 0.6 MJ/L (2100 BTU/gal) of electricity are required to run the pumps, motors, and certain unit operations such as the molecular sieve and dewatering press. Comparing to the dry grind process, the results demonstrated that the QQ ethanol process achieves a 31.6% reduction in total energy use (Fig. 3). While the QQ process runs more unit operations and has a higher electricity demand than the dry grind process, the significant steam savings in the QQ process offset its higher electricity demand. The energy demand results derived here are comparable to two previously published industrial surveys (Shapouri and Gallagher, 2005; Wu, 2008). The results showed that the energy demand of the dry grind process derived from the simulation is higher than both in the survey results. This can be explained by the fact that many plants do not dry the stillage before selling DDGS, due to the huge energy requirement. The lower energy demand in 2007 survey compared to 2002 survey is partially because it was reported that more than one third of the DDGS are sold as wet feeds (Wu, 2008). If there were no DDGS drying in the simulation model, the energy demand of the dry grind process will be lower than that from Wu (2008), as effectively there would be no natural gas requirement and only steam and electricity demands would remain. The comparison validates that the simulation model is capable of representing industrial performance. Considering the lower energy demands by the QQ process, it is anticipated that the energy demand for ethanol production can be further
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993 Table 3 Operating data for the distillation sub-system in the QQ and conventional dry grind processes. Process step Beer column inlet Ethanol concentration Loading rate (kg/h) Rectifier column inlet Ethanol concentration Loading rate (kg/h) Stripper column inlet Ethanol concentration Loading rate (kg/h)
QQ
Dry grind
15% 118,926
10.9% 138,899
59.5% 35,865
51.4% 35,335
0.9% 12,992
0.6% 15,327
reduced if more plants adopt and continue to develop this novel technology. Due to the germ and fiber recovery, the ethanol concentration of the inlet stream of the beer column is improved to 15% (m/m) from 10.9% (m/m) in the dry grind process (Table 3). The increased ethanol concentration is the major contributor for the energy demand reduction achieved by the QQ process. A detailed energy demand comparison between the two processes is provided in Fig. S1 in the Supplementary Material on the Web. 3.2. Water demand The total water demand of the QQ process is reduced to 3.49 L for each liter ethanol production (3.49 L/L), as compared to 4.25 L/L in the dry grind process. The results derived from simulation models are comparable with industrial surveys, as shown in Fig. 4. Because the water used for the germ and fiber washing can be recycled and reused, the QQ process does not require higher process water input rate—0.43 kg of water per kg of corn processed—despite the fact that it adds a germ and fiber recovery system at the front end. However, because of its lower ethanol yield, the process water demand of the QQ process is effectively a little higher than that of the dry grind process: 1.06 as compared to 1.04 L/L, respectively. The detailed input-output analysis of process water is shown in Fig. 5. There is only one major fresh process water input, and it is fed into the CO2 scrubber. After recovering the emitted ethanol in the CO2 scrubber, this process water is routed back to the soaking tank to provide the water for soaking and the following unit operations. In addition, corn is brought into the system with 14.5% moisture content that provides 0.36 L/L of water to the system. The cooling water demand of QQ process has been reduced to 2.43 L/L, compared with 3.21 L/L by the dry grind process (Fig. 6).
Fig. 4. A comparison of consumptive water usage in the QQ and dry grind processes determined here and contrasted with previous surveys: (†) Keeney and Muller (2006). (The results were derived from a survey of average water use by ethanol plants located in Minnesota); (‡) Shapouri and Gallagher (2005). (The results were based on USDA 2002 survey); () Wu (2008). (The results were based on RFA 2007 survey, which indicated that the water consumption ranges from 2.65 to 4.9 L/L, with an average of 3.45 L/L).
991 Process water input (1.06 liter/liter)
Dryer loss (1.17 liter/liter) Corn (0.36 liter/liter)
QQ process
Saccharification (0.16 liter/liter) Germ (0.02 liter/liter)
Fiber DDGS (0.02 liter/liter) (0.04 liter/liter)
Fig. 5. The process water input–output analysis in the QQ process.
The savings of cooling water used for evaporator vapor recapture as well as distillation are the major contributors for the cooling water reduction in the QQ process. Due to the germ and fiber recovery at the front end, the amount of thin stillage loading to the evaporator in the QQ process is considerable lower than that in the dry grind process. Because the cooling water demand is proportional to the vapor loss rate, the lower loading rate of evaporator in the QQ process results in a lower vapor loss, and it thus leads to a lower cooling water demand to recapture the vapor loss. Approximately 0.42 L/L savings are achieved in the QQ process. The higher ethanol concentration facilitates the separation in the distillation system. Approximately 0.22 L/L of cooling water savings are achieved in the beer column of the QQ process. However, due to its lower loading rate, the savings in the rectifier column are lower than that in the beer column. Because ethanol concentrations are similar, the cooling water demands are comparable in the stripping column. To facilitate the yeast propagation, a cooler is set to cool the mash down to 30 ◦ C before feeding into the fermentation tank. The separation of germ and fiber at the front end reduces the materials processed to the cooler before fermentation, therefore providing 0.07 L/L savings of cooling water in the QQ process. 3.3. Product evaluation Regarding dry matter input, 100 kg of corn dry matter is fed into the process with 0.36 kg of urea. The total output of the QQ process is produced at the rate of 107.98 kg per 100 kg corn dry matter input. The difference between the input and output is due to the saccharification reaction where approximately 7.49 kg of process water are consumed for every 100 kg of dry corn.
Fig. 6. A cooling water demand comparison between the QQ and the conventional dry grind processes.
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Table 4 A coproduct quality comparison between the QQ and the conventional dry grind processes. QQ
Oil Protein NDF Starch Sugar Ash Other
Dry grind
Germ
Fiber
DDGS
DDGS
43.1% 15.4% 28.7% 7.6% 2.4% 2.6% 0.2%
8.9% 8.6% 69.9% 10.0% 0.9% 1.3% 0.5%
0.6% 46.0% 17.1% 7.4% 20.4% 4.2% 4.5%
12.9% 32.3% 32.1% 4.4% 12.1% 3.4% 2.8%
Ethanol and CO2 are the major products, each accounting for approximately one third of the total output. The remaining one third of the dry matter is incorporated in the solid coproducts: germ, fiber, and DDGS. Among these products, ethanol yield rate is the most important economic factor for an ethanol facility. Ethanol is produced at the rate of 0.405 L/kg of corn input (2.72 gal/bu) in the QQ process, which is lower than 0.414 L/kg (2.78 gal/bu) by the dry grind process. This is due to starch losses in the germ and fiber recovery process. After separation, the germ fraction contains 43% oil (d.b.) and can be further used for oil extraction; the fiber fraction is composed of 70% NDF (d.b.). In addition, a small amount of starch and sugars is recovered in both the germ and fiber streams. The starch accounts for 8% of germ fraction and 10% of fiber fraction, respectively. After germ and fiber recovery, all other nonfermantable materials along with some unconverted starch and sugar end in DDGS. Detailed compositions of germ, fiber, and DDGS streams are shown in Table 4.
Table 4 also compares the composition of DDGS produced in the dry grind and the QQ process. The prefractionation in the QQ process produces DDGS with higher protein and lower fiber content, thus making it an amenable feed for non-ruminant animals. In addition, the improved nutritional characteristics of DDGS may demand a premium price, further improving the economics of the ethanol facility if an optimistic market rewarding higher quality of DDGS comes to exist. 3.4. Economic performance 3.4.1. Baseline analysis An economic performance comparison between the dry grind plant and the QQ plant is given in Table 5. The comparison showed that the QQ plant requires a total capital investment of $96.2 million, 26.5% higher than that of the dry grind plant. The higher capital investment costs for the QQ plant are due to the additional germ and fiber recovery system. The annual raw material costs for the QQ plant are 20% higher than that for the dry grind plant, which corresponds to an increased processing capacity. Due to the lower ethanol yield rate, the QQ plant increases its unit raw material cost by 2.2%. Despite its increased processing capacity, the QQ plant demands less annual utility costs than the dry grind plant. Although the QQ process requires more electricity to power additional equipment, it provides significant savings in steam demand of distillation due to the germ and fiber recovery. The QQ plant reduces its utility costs for each gallon of ethanol production by 21.4%, which corresponds to findings in the energy demand analysis. The QQ plant requires higher annual operating costs due to its increased processing capacity. However, the unit operating costs for these two plants are comparable, both of which are approx-
Table 5 An economic comparison between a conventional dry grind plant and a QQ plant. Conventional dry-grind plant
QQ plant
$1000/yr
¢/L
¢/gal
¢/L
¢/gal
60,283 76,443
36.52 46.31
138.23 175.29
77,222 96,182
39.89 49.68
150.97 188.04
64,226 1500 190 474 63 1237 67,690
38.91 0.91 0.12 0.29 0.04 0.75 41.01
147.28 3.44 0.44 1.09 0.14 2.84 155.22
77,084 1790 234 532 70 1451 81,161
39.82 0.92 0.12 0.27 0.04 0.75 41.92
150.70 3.50 0.46 1.04 0.14 2.84 158.67
Utilities: Electricity Natural gas Steam Cooling water Subtotal
1989 2555 5521 181 10,246
1.21 1.55 3.34 0.11 6.21
4.56 5.86 12.66 0.42 23.49
2450 2579 4247 166 9442
1.27 1.33 2.19 0.09 4.88
4.79 5.04 8.30 0.32 18.46
Labor dependent Facility dependent Depreciation Operating costs
2898 2743 4019 87,596
1.76 1.66 2.43 53.07
6.65 6.29 9.22 200.86
5519 3514 5148 104,784
2.85 1.82 2.66 54.12
10.79 6.87 10.06 204.85
Products: Ethanol DDGS Corn germ Corn fiber Revenues
74,991 17,632 – – 92,623
45.43 10.68 – – 56.11
171.96 40.43 – – 212.39
87,956 11,809 13,141 2732 115,638
45.43 6.10 6.79 1.41 59.73
171.96 23.09 25.69 5.34 226.07
Gross profit (A) Taxes (40%) (B) Depreciation (C) Annual cash flow (A − B + C) Payback period (years)
5027 2011 4019 7035 10.9
3.05 1.22 2.43 4.26
11.53 4.61 9.22 16.13
10,854 4342 5148 11,661 8.3
5.61 2.24 2.66 6.02
21.22 8.49 10.06 22.80
Fixed capital costs Capital investment costs Raw materials: Corn Enzymes Yeasts Other chemicals Process water Denaturant Subtotal
$1000/yr
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993
993
Table 6 An economic comparison of the impact of investor ownership and loan requirements on cash flow in conventional dry grind and QQ processes. 12% dividends + 100% equity
12% dividends + 40% equity
Conventional dry grind
QQ
Conventional dry grind
$1000/yr
$1000/yr
QQ
¢/L
¢/gal
¢/L
¢/gal
¢/L
¢/gal
¢/L
¢/gal
Capital investment costs Equity Principle
76,443 76,443
46.31 46.31
175.29 175.29
96,182 96,182
49.68 49.68
188.04 188.04
76,443 30,577 45,866
46.31 18.52 27.79
175.29 70.12 105.17
96,182 38,473 57,709
49.68 19.87 29.81
188.04 75.21 112.82
Amortized payments Operating Costs Revenues
– 87,596 92,623
– 53.07 56.11
– 200.86 212.39
– 104,784 115,638
– 54.12 59.73
– 204.85 226.07
6030 93,626 92,623
3.65 56.72 56.11
13.83 214.69 212.39
7587 112,371 115,638
3.92 58.04 59.73
14.83 219.69 226.07
Gross Profit (A) Taxes (40%) (B) Depreciation (C)
5027 2011 4019
3.05 1.22 2.43
11.53 4.61 9.22
10,854 4342 5148
5.61 2.24 2.66
21.22 8.49 10.06
−1003 0 4019
−0.61 0.00 2.43
−2.30 0.00 9.22
3267 1307 5148
1.69 0.67 2.66
6.39 2.55 10.06
Dividends (D) Annual cash Flow (A − B + C − D) Payback period (years)
6115 920 83.1
3.70 0.56
14.02 2.11
7695 3966 24.3
3.97 2.05
15.04 7.75
2446 570 53.6
1.48 0.35
5.61 1.31
3078 4030 9.6
1.59 2.08
6.02 7.88
imately 54 ¢/L. This is due to the significant utility savings of the QQ plant, which offset higher depreciation and raw material costs. The coproducts provide a significant revenue boost for the QQ plant as compared to the dry grind plant. Although the revenue of DDGS in the QQ plant is reduced because of the prefractionation process, more value-added coproducts, such as germ and fiber, are produced. These factors taken together with the increased ethanol production cause the total revenues of the QQ plant to increase by almost $23 million. Assuming no external loans are required for both ethanol plants, the QQ plant has a higher annual cash flow. Despite its higher capital investment cost, the payback period of the QQ plant is reduced to 8.3 years, compared to 10.9 years for the dry grind plant. Increased processing capacity, more value-added coproducts, and reduced utility costs are three major factors boosting economic performance of the QQ plant. 3.4.2. Scenario analysis Table 6 outlines the results from two additional economic scenarios beyond the baseline case. These results suggest that the QQ facility will be considerably better in both scenarios. When investors seek outside funding, gross profits are reduced due to the additional amortized payments, while cash flow is increased because of the significant reduction of the dividends. The impact of seeking outside funding is particularly attractive for the QQ facility if the loan interest is 10% and dividend rate is 8%, where the payback period is reduced to 9.6 from 24.3 years. 4. Conclusion Comparing the QQ and the dry grind process models, the result shows that the QQ process reduces energy demand by 31.6% and water demand by 1.76 L/L, significantly as a result of its increased ethanol concentration in the beer. The QQ process produces more value-added coproducts, but has a lower ethanol yield rate (0.405 as compared to 0.414 L/kg). Because of its reduced utility costs and increased revenue by its value-added coproduct sell, the QQ process has a shorter payback period as compared to the conventional dry grind process. Acknowledgements The authors would like to thank Dr. Madhu Khanna and Dr. K.C. Ting for their help in preparing this work. This work was funded by
$1000/yr
$1000/yr
the Illinois Council on Food and Agricultural Research (C-FAR) and University of Illinois. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.indcrop.2011.03.003. References Asano, T., Burton, F.L., Leverenz, H., Tsuchihashi, R., Tchobanoglous, G., 2007. Water Reuse: Issues Technologies, and Applications. McGraw-Hill, New York. Douglas, J.M., 1988. Conceptual design of chemical processes. McGraw-Hill, New York. Farrell, A.E., Plevin, R.J., Turner, B.T., Jones, A.D., O’Hare, M., Kammen, D.M., 2006. Ethanol can contribute to energy and environmental goals. Science 311 (5760), 506–508. Keeney, D. R., Muller, M. 2006. Water use by ethanol plants – potential Challenges. Available at: www.agobservatory.org/library.cfm?refid=89449. (accessed 10/ 20/ 2009). Kwiatkowski, J.R., McAloon, A.J., Taylor, F., Johnston, D.B., 2006. Modeling the process and costs of fuel ethanol production by the corn dry-grind process. Ind. Crop. Prod. 23 (3), 288–296. Li, C., Rodríguez, L.F., Khanna, M., Spaulding, A.D., Lin, T., Eckhoff, S.R., 2010. An engineering and economic evaluation of quick germ quick fiber process for dry-grind ethanol facilities: model description and documentation. Bioresour. Technol. 101 (14), 5275–5281. Pimentel, D., Patzek, T.W., Cecil, G., 2007. Ethanol production: energy, economic, and environmental losses. Rev. Environ. Contam. Toxicol. 189, 25–41. Ramirez, E.C., Johnston, D.B., McAloon, A.J., Yee, W., Singh, V., 2008. Engineering process and cost model for a conventional corn wet milling facility. Ind. Crop. Prod. 27 (1), 91–97. Rodríguez, L.F., Li, C., Khanna, M., Spaulding, A.D., Lin, T., Eckhoff, S.R., 2010. An engineering and economic evaluation of quick germ-quick fiber process for drygrind ethanol facilities: analysis. Bioresour. Technol. 101 (14), 5282–5289. Singh, V., Eckhoff, S.R., 1996. Effect of soak time, soak temperature, and lactic acid on germ recovery parameters. Cereal Chem. 73 (6), 716–720. Singh, V., Johnston, D.B., Naidu, K., Rausch, K.D., Belyea, R.L., Tumbleson, M.E., 2005. Comparison of modified dry-grind corn processes for fermentation characteristics and DDGS composition. Cereal Chem. 82 (2), 187–190. Singh, V., Moreau, R.A., Doner, L.W., Eckhoff, S.R., Hicks, K.B., 1999. Recovery of fiber in the corn dry-grind ethanol process: a feedstock for valuable coproducts. Cereal Chem. 76 (6), 868–872. Shapouri, H., Gallagher, P., 2005. USDA’s 2002 Ethanol Cost-of-Product Survey: Agricultural Economic Report Number 841. US Department of Agriculture, DC, Washington. Tiffany, D.G., Eidman, V.R., 2003. Factors associated with success of fuel ethanol producers. No. Staff Paper P03-7. In: College of Agriculture, Food, and Environmental Sciences. Department of Applied Economics, University of Minnesota. Watson, S.A., 1987. Structure and composition. In: Watson, S.A., Ramstad, P.E. (Eds.), Corn: Chemistry and technology. Am. Assoc. Cereal Chem., Inc., St. Paul, p. 69. Wu, M. 2008. Analysis of the efficiency of the U.S. ethanol industry 2007. Argonne, IL: Argonne National Laboratory. http://www.ethanol.org/pdf/ contentmgmt/2007 analysis of the efficiency of the us ethanol industry.pdf. (accessed 10/ 04/ 2010). Wu, M., Mintz, M., Wang, M., Arora, S., 2009. Consumptive Water Use in the Production of Ethanol and Petroleum Gasoline. Center for Transportation Research, Argonne National Laboratory ANL/ESD/09-1.
Contents lists available at ScienceDirect
Industrial Crops and Products journal homepage: www.elsevier.com/locate/indcrop
Process modeling of the Quick-germ/Quick-fiber process: Energy, water, and economics Tao Lin a,1 , Luis F. Rodríguez b,∗ , Steven R. Eckhoff c,2 a b c
University of Illinois at Urbana-Champaign, 374 Agricultural Engineering Sciences Building, MC-644, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, United States University of Illinois at Urbana-Champaign, 376C Agricultural Engineering Sciences Building, MC-644, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, United States University of Illinois at Urbana-Champaign, 360C Agricultural Engineering Sciences Building, MC-644, 1304 W. Pennsylvania Avenue, Urbana, IL 61801, United States
a r t i c l e
i n f o
Article history: Received 21 December 2010 Received in revised form 1 March 2011 Accepted 3 March 2011 Available online 6 May 2011 Keywords: Process simulation model Quick-germ/Quick-fiber process Water balance Energy balance Economic Bioenergy
a b s t r a c t A process simulation model was developed on the SuperPro Designer® platform for a Quick-germ/Quickfiber (QQ) modified dry grind ethanol fractionation facility. This paper compares energy and water demands, product quality, and economic performance between the QQ and the dry grind ethanol processes. Results showed that the QQ process reduces energy demand by 31.6% and water demand by 17.9%, produces more value-added coproducts, and improves the economic viability. The QQ process has a lower ethanol yield (0.405 vs. 0.414 L of ethanol per kg of corn) because of starch loss at the front-end separation. This model can be used to provide decision support for ethanol producers considering the new emerging technology. © 2011 Elsevier B.V. All rights reserved.
1. Introduction Currently, corn is the primary feedstock source providing ethanol in the US; however, it has been widely debated whether ethanol produced from corn is sustainable in the long term (Farrell et al., 2006; Pimentel et al., 2007). Environmentally, the major concern is that producing ethanol from corn demands intensive water and energy consumption. Economically, recent fluctuations in petroleum, ethanol, and corn prices have driven several large producers of ethanol into bankruptcy or acquisition.3 The ethanol industry is vulnerable to periods of economic weakness because its product value varies with oil prices but its raw material (corn) cost varies with food prices. When ethanol prices are strong, dry grind processors focus on how to improve ethanol yield, because coproduct values are not as significant (Rodríguez et al., 2010). However, when corn prices are high and ethanol prices are low, the dry grind processors lose money rapidly (Tiffany and Eidman,
∗ Corresponding author. Tel.: +1 217 333 2694; fax: +1 217 244 0323. E-mail addresses: [email protected] (T. Lin), [email protected] (L.F. Rodríguez), [email protected] (S.R. Eckhoff). 1 Tel.: +1 217 333 2694; fax: +1 217 244 0323. 2 Tel.: +1 217 244 4022, fax: +1 217 244 0323. 3 Wall Street Journal. 2009. VeraSun Seeks Bankruptcy Protection. Available at: http://online.wsj.com/article/SB122552670080390765.html. Accessed 20 December 2009. 0926-6690/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.indcrop.2011.03.003
2003; Rodríguez et al., 2010). Wet milling is a more stable method for ethanol production because its coproduct values usually follow corn price trends. When corn prices are high, coproduct value increases to offset the lower ethanol prices. To stabilize ethanol production, dry grind ethanol processors need to develop their coproducts in order to get through the times when ethanol prices are low and corn prices are high. In response to this need, several modified dry grind processes have been developed to improve the profitability of ethanol production; significant improvements have been observed on the processing efficiency and the nutritional characteristics of coproducts at the laboratory scale (Singh et al., 2005). The Quickgerm/Quick-fiber (QQ) process (Singh and Eckhoff, 1996; Singh et al., 1999) is one of these modified dry grind processes. It adapts a part of the wet milling process (the germ and fiber recovery system) for the dry grind process. This process has three key advantages: recovered fiber and germ can be further processed to generate value-added coproducts such as germ oil, corn fiber oil, and corn fiber gum; removal of germ and fiber can increase the protein content of distiller’s dried grains with solubles (DDGS), making this coproduct suitable as a feed for non-ruminant animals such as swine and poultry; separating the nonfermentable fractions before fermentation will improve the process efficiency by 14% (Singh et al., 1999, 2005). Recently, Li et al. (2010) and Rodríguez et al. (2010) developed a detailed engineering economic spreadsheet model that showed that the QQ process improves
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993
987
Clean Corn Soaking Vent Grinding Germ and Fiber
Germ & Fiber Separator
Dryer
Liquefaction
Jet Cook
Cooler
Saccharification CO2 Scrubber
Fermentation
CO2
Fresh Water Distillation
Dehydration
Whole Stillage
Thin Stillage
Ethanol
Centrifugation Wet Cake
Separator Slurry
Evaporation
Liquid Gas
Vent
Dryer
DDGS
Fig. 1. A simplified flow sheet of the QQ process.
the economic viability of the dry grind process at the commercial scale. Although these previous studies showed the QQ process is economically viable, there is no analysis that details the energy and water consumption of the QQ process and compares the process performance to the dry grind process. The objective of this study was to develop a simulation model of the QQ process on the SuperPro Designer® platform to predict operating performance at the commercial scale using a continuous process and compare it to the USDA’s dry grind model (Kwiatkowski et al., 2006). This study identifies and compares the energy consumption, water usage, product compositions, and economic performance of the QQ and the dry grind processes.
2. Methods 2.1. Process model description The QQ process model developed in this study is based on the USDA’s dry grind (Kwiatkowski et al., 2006) and wet milling models (Ramirez et al., 2008); the previous dry grind and wet milling models are developed on the SuperPro Designer platform as is the model developed here. This work is augmented by the previous work of the investigators (Li et al., 2010; Rodríguez et al., 2010). The dry
grind model developed by USDA is designed to process 1.12 million kg of corn per day (43,800 bu/day) (Kwiatkowski et al., 2006). Because of the separation of corn germ and fiber at the front end, a higher rate of corn input can be processed with the same size of equipment used in the dry grind process (Singh et al., 2005). By adding a germ and fiber separation system to the dry grind process, a QQ process model is developed to process 1.34 million kg of corn per day (52,500 bu/day), without increasing the size of the equipment used in the dry grind process designed by Kwiatkowski et al. (2006). Both plants operate 24 h per day and 350 days per year, with time set aside for maintenance and repairs. The QQ process consists of six major sub-systems: grain cleaning, germ and fiber recovery, liquefaction and saccharification, fermentation, distillation, and DDGS recovery. A simplified flow sheet is provided in Fig. 1. Detailed information regarding process requirements are available in Kwiatkowski et al. (2006); only significant changes specific to the QQ process model are detailed here: the consideration of corn composition as impacted by the germ and fiber recovery sub-system, and the fermentation sub-system.
2.1.1. Corn composition Assuming 14.5% moisture content, 1 bushel, or 25.5 kg, of corn will have 21.8 kg (48.0 lb) of dry materials (Watson, 1987). Corn kernels can be divided into three major parts: pericarp and tip cap;
988
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993 Washing Water
Germ and Fiber Stream 1st Screen
Mixer
Dewatering Screw 2nd Screen
Blending Tank
3rd Screen Send to the fluidized bed dryer
Process Water
Corn Mixer
Corn Slurry Soaking Tank 1st Grinding
1st Grind Tank
Hydrocyclone 1 Corn slurry after separation Hydrocyclone 2 2nd Grind
Send to fermentation
Fig. 2. A detailed view of the germ and fiber recovery sub-system.
endosperm; and germ. Starch, oil, neutral detergent fiber (NDF), protein, sugars, and ash are the major constituents making up the corn kernel and are each distributed throughout the three parts. To better describe the coproducts recovered via the QQ process, the protein and NDF are further divided based on solubility and their location within each part of the corn kernel. Detailed information regarding the corn composition is provided in Table S1 in the Supplementary Material on the Web. 2.1.2. Germ and fiber recovery sub-system The germ and fiber recovery sub-system, shown in Fig. 2, involves the soaking of the corn kernel for 8 h, a two-stage grind, germ and fiber recovery using hydrocyclones, three-stages of germ and fiber washing, and germ and fiber separation and dewatering. The moisture content of corn increases from 14.5 to 57.4% during soaking. The separation system is based on the density difference between the constituent parts of corn. It consists of two stages of hydrocyclones and three stages of wash screens. The diluted slurry from the mill is pumped to hydrocyclones, where the lighter parts, germ and fiber, are floated off the top. The washing system consists of three gravity screens used to wash the loose starch and protein from the germ and fiber. Water is recycled from the last two stages of washing. The wash water finally leaves in the underflow of the first screen with the free starch and protein. The detailed operational information of the equipment, such as top flow ratio and output dry mass, is provided in Table S2 in the Supplementary Material on the Web. Based on the ratio set in this model, approximately 95% of pericarp fiber and 90% of germ are separated from the corn slurry. After washing, the germ and fiber are then dewatered in the screw press to an average of 50% moisture content. Germ and fiber are further dried and separated via a fluidized bed dryer (not
pictured) to 3% and 10% moisture content, respectively. The outlet stream of germ and fiber are produced at a rate of 4228 and 3871 kg/h, respectively. 2.1.3. Fermentation sub-system Fermentation is a conversion step where glucose is converted into ethanol and carbon dioxide with yeast. Urea is added at the rate of 0.3% (wet basis) of corn input to provide a nitrogen source for the yeast propagation. The fermentation simulated in this process is continuous, and the residence time is set at 60 h. In this model, 91.9% of total glucose is assumed to be converted to ethanol and carbon dioxide, on a mass basis: C6 H12 O6 → 0.511 C2 H5 OH + 0.489 CO2 .
(1)
In addition to the glucose consumed in Eq. (1), 3.28% of the initial glucose is consumed for the yeast propagation, and carbon dioxide is produced via yeast metabolism: 0.88C6 H12 O6 + 0.12(NH2 )2 CO → 0.742YeastDryMatter + 0.258CO2 .
(2)
The remaining 4.82% of the glucose is not involved in the conversion and remains in the DDGS. The extent of conversation is based on industrial data and research data. 2.2. Cost model description A cost model was developed on the SuperPro Designer® platform to evaluate the economic performance of the QQ process. Capital investment costs, operating costs, and product values are vital for the economic analysis.
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993
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Table 1 Total capital investment costs for the QQ process. Factor of purchased equipment costa
Cost (Million $)
Cost ($/L)
Cost ($/gal)
Purchased equipment Purchased equipment installation Piping Instrumentation and control Electrical equipment and material Building Yard improvement Total direct costs
100% 40% 30% 10% 15% 40% 15% 250%
24.13b 9.65 7.24 2.41 3.62 9.65 3.62 60.33
0.12 0.05 0.04 0.01 0.02 0.05 0.02 0.31
0.47 0.19 0.14 0.05 0.07 0.19 0.07 1.18
Engineering Construction Total indirect costs
25% 45% 70%
6.03 10.86 16.89
0.03 0.06 0.09
0.12 0.21 0.33
Fixed capital costs Working capital costs Startup capital costs Total capital investment costs
320% 48% 32% 400%
77.22 11.24 7.72 96.18
0.40 0.06 0.04 0.50
1.51 0.22 0.15 1.88
a b
Douglas (1988). The purchased equipment cost is estimated by the QQ process model developed here.
2.2.1. Capital investment costs Capital investment costs are estimated based on the purchased costs of each piece of operating equipment. Based on the cost estimation factors developed by Douglas (1988), the total capital investment cost would be four times the total equipment purchased costs (Table 1). The equipment costs are based on the SuperPro Designer® equipment cost estimating parameters as well as USDA’s dry grind and wet milling models (Kwiatkowski et al., 2006; Ramirez et al., 2008), and the equipment costs are adjusted for capacity using standard engineering scaling factors. 2.2.2. Operating costs Operating costs of an ethanol plant consist of raw materials, utility, labor, and facility dependent costs. Based on the processing rate, the amount of raw materials and utilities required by the plant can be calculated by the model simulation. The unit costs of the raw materials and utilities are the input values, shown in Table 2. These costs are based on the market prices observed in April 2009 as well as the literature (Kwiatkowski et al., 2006; Li et al., 2010). Facility dependent costs include maintenance costs, insurance, local taxes, and other factory expenses. Maintenance costs are assumed to be 3% of direct fixed capital costs, while insurance and other factory expenses are at 0.85% and 0.7% of direct fixed capital costs, respectively (Kwiatkowski et al., 2006). Depreciation and amortized loan payments are generally associated with the facility dependent costs. The QQ plant considered here is assumed to be financed without any external loans, and thus no amortized loan payment is needed. The plant has a 15-year lifetime with zero salvage value at the end. The annual depreciation cost is calculated via the straight-line method.
Eight operators and one supervisor are assumed to work full time in this QQ ethanol facility, and five operators are assumed to work in the dry grind plant (Kwiatkowski et al., 2006). The unit labor costs of an operator and a supervisor are at an all-inclusive rate of $69/h and $105/h, respectively. 2.2.3. Products values In the QQ process, ethanol, DDGS, germ, and fiber are the four products marketed, whereas CO2 , a potentially marketable product, is assumed to be purged to atmosphere. The prices of ethanol, DDGS, and corn oil in this analysis are based on the market prices as observed in April 2009 and are shown in Table 2. The value of the fiber fraction is set at $0.08 per kg based on its fraction within the DDGS and DDGS prices. The value of the germ fraction is set at $0.37 per kg, based on the corn oil and protein values and oil extraction costs (A detailed germ value calculation is provided in the Supplemental Materials). 2.3. Energy demand analysis The stream data related to the heating demand includes the specific heat capacity of raw materials, temperature, and flowrate for each process stream, as well as the residence time of each unit operation. With the aid of simulation software (SuperPro Designer® ), the model calculates the specific heat of each stream based on its composition. Corn is the single major dry matter input in the process. To facilitate the comparison of two dry grind models consistently, the specific heat capacity of each corn component in the QQ model is based on the assumptions used in the USDA’s dry grind model (Kwiatkowski et al., 2006). Under these assumptions, the QQ
Table 2 Unit operating cost and product value of raw materials, utilities, and products in the QQ process. Unit Cost Raw materials Corn Alpha amylase Glucoamylase Yeast Urea Succinic acid Process water Denaturant
$4.2/Bua $2.25/kgb $2.25/kgb $1.86/kgb $0.31/kgc $0.11/kgb $0.35/kLb $0.475/kgc
Unit cost Utilities Natural gas Steam Electricity Cooling Water
$4 per MMBtuc $5 per MMBtud $0.078/kWhc $0.35/kLb
Unit value Products Ethanol DDGS Germ Fiber
$1.72/gala $130/tona $0.37/kg $0.08/kgc
a Market prices as observed in April 2009: corn price: http://www.ams.usda.gov/mnreports/gx gr115.txt (accessed on May 14, 2009); DDGS price: http://www.ams.usda.gov/mnreports/sj gr225.txt (accessed on May 14, 2009); ethanol price: http://www.ethanolmarket.com/fuelethanol.html (accessed on May 14, 2009). b Kwiatkowski et al. (2006). c Li et al. (2010). d Purchased at 1.25 times the natural gas cost.
990
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993
process model quantifies the heating duty of each unit operation based on its operational requirements. The operational parameters, such as temperature, moisture content and residence time, are input values and can be easily modified to suit user preferences. The temperature and moisture content of several major streams in the QQ process are provided in Table S3 in the Supplementary Material on the Web. Steam and cooling water are used to adjust working temperature of each unit operation, while natural gas is exclusively used for the dryer unit operation. 2.4. Water demand analysis The water demand of corn-to-ethanol production can be composed of two parts: process water and non-process water demand. The process water is the water directly mixed with ground corn to form the slurry. For the process water demand analysis, based on the feedstock input rate and the moisture content requirement of each processing stream, the simulation model would quantify the process water input rate. Cooling water and steam water are two types of non-process water in ethanol production. As steam is purchased as a utility, not generated at the site in this model, steam water is not considered in this analysis. Based on the previous study, steam water only accounts for less than 3 percent of total water demand (Wu et al., 2009). Therefore, the exclusion of steam water demand would not have a major impact on the total water demand analysis. The cooling water considered here is a recirculating noncontact system, where the cooling water losses mainly occur in the cooling tower via evaporation, blowdown, and drift. In a welloperated recirculating cooling system, blowdown and drift water loss account for only a small portion of cooling water usage (Asano et al., 2007). To simplify the analysis, the cooling water demand is based on the cooling water evaporation rate. Based on the cooling requirement in each unit operation, the simulation model quantifies the circulation flowrate of the cooling water. In the QQ process model, the supply and return temperature of cooling water are designed at 15 and 26 ◦ C, respectively. Based on a simplified mass and energy equation (Asano et al., 2007), 2300Qe = 4.2Qc T
(3) rate.4
the evaporation rate would be 2% of the circulation Qe is the evaporation rate (kg/h); Qc is the circulation rate (kg/h); T is the temperature change (◦ C). Without considering the blowdown and drift loss in this analysis, the makeup cooling water usage consists of 2% of the circulation flowrate. 2.5. Economic analysis A baseline scenario is selected based on the input information described in the cost model description section. No external loans or dividend is assumed for the baseline analysis. Therefore, the annual cash flow is determined by the sum of the income after tax as well as depreciation. The analysis does not consider the impacts of inflation during the lifetime of the facility, thus it would be considered in real terms. The payback period for the QQ facility is determined as a ratio of total capital investment cost to annual cash flow. Two additional economic scenarios are considered as well. First, an ownership group is presumed to provide 100% of the capital requirement. The investors are presumed to collect dividends rated at 8% of the original equity invested. Dividends are subtracted from the cash flow calculation described above. Second, building from
4 Note: 2300 kJ is required to evaporate 1 kg of water, while 4.2 kJ is required to cool 1 kg of water by 1 ◦ C.
Fig. 3. A comparison of the energy demand in the QQ and dry grind processes determined here, as compared to previous industrial surveys: USDA 2002 survey: Shapouri and Gallagher (2005); RFA 2007 survey: Wu (2008).
the investor scenario, those investors are presumed to provide 40% of the initial capital, thus requiring the remaining funds to be acquired via loan. The interest rate of the load is assumed to be 10%, with payments amortized over the period of 15 years. Amortized loan payments are presumed to be operating costs for the facility, thus affecting net profit. The payback period is determined as a ratio of the equity by the investors to annual cash flow. 3. Results and discussion 3.1. Energy demand Based on the QQ simulation model, the QQ process utilizes 8.7 MJ of energy to produce each liter of ethanol (31,342 BTU/gal) (Fig. 3). Steam is the largest required energy resource in the process, accounting for more than half of the total energy demand. A significant amount of steam is used to provide heat in the distillation and liquefaction unit operations. Natural gas is the second largest energy source used to dry the coproducts including germ, fiber, and DDGS, which accounts for approximately 3.6 MJ/L (12,880 BTU/gal). Approximately 0.6 MJ/L (2100 BTU/gal) of electricity are required to run the pumps, motors, and certain unit operations such as the molecular sieve and dewatering press. Comparing to the dry grind process, the results demonstrated that the QQ ethanol process achieves a 31.6% reduction in total energy use (Fig. 3). While the QQ process runs more unit operations and has a higher electricity demand than the dry grind process, the significant steam savings in the QQ process offset its higher electricity demand. The energy demand results derived here are comparable to two previously published industrial surveys (Shapouri and Gallagher, 2005; Wu, 2008). The results showed that the energy demand of the dry grind process derived from the simulation is higher than both in the survey results. This can be explained by the fact that many plants do not dry the stillage before selling DDGS, due to the huge energy requirement. The lower energy demand in 2007 survey compared to 2002 survey is partially because it was reported that more than one third of the DDGS are sold as wet feeds (Wu, 2008). If there were no DDGS drying in the simulation model, the energy demand of the dry grind process will be lower than that from Wu (2008), as effectively there would be no natural gas requirement and only steam and electricity demands would remain. The comparison validates that the simulation model is capable of representing industrial performance. Considering the lower energy demands by the QQ process, it is anticipated that the energy demand for ethanol production can be further
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993 Table 3 Operating data for the distillation sub-system in the QQ and conventional dry grind processes. Process step Beer column inlet Ethanol concentration Loading rate (kg/h) Rectifier column inlet Ethanol concentration Loading rate (kg/h) Stripper column inlet Ethanol concentration Loading rate (kg/h)
Dry grind
15% 118,926
10.9% 138,899
59.5% 35,865
51.4% 35,335
0.9% 12,992
0.6% 15,327
reduced if more plants adopt and continue to develop this novel technology. Due to the germ and fiber recovery, the ethanol concentration of the inlet stream of the beer column is improved to 15% (m/m) from 10.9% (m/m) in the dry grind process (Table 3). The increased ethanol concentration is the major contributor for the energy demand reduction achieved by the QQ process. A detailed energy demand comparison between the two processes is provided in Fig. S1 in the Supplementary Material on the Web. 3.2. Water demand The total water demand of the QQ process is reduced to 3.49 L for each liter ethanol production (3.49 L/L), as compared to 4.25 L/L in the dry grind process. The results derived from simulation models are comparable with industrial surveys, as shown in Fig. 4. Because the water used for the germ and fiber washing can be recycled and reused, the QQ process does not require higher process water input rate—0.43 kg of water per kg of corn processed—despite the fact that it adds a germ and fiber recovery system at the front end. However, because of its lower ethanol yield, the process water demand of the QQ process is effectively a little higher than that of the dry grind process: 1.06 as compared to 1.04 L/L, respectively. The detailed input-output analysis of process water is shown in Fig. 5. There is only one major fresh process water input, and it is fed into the CO2 scrubber. After recovering the emitted ethanol in the CO2 scrubber, this process water is routed back to the soaking tank to provide the water for soaking and the following unit operations. In addition, corn is brought into the system with 14.5% moisture content that provides 0.36 L/L of water to the system. The cooling water demand of QQ process has been reduced to 2.43 L/L, compared with 3.21 L/L by the dry grind process (Fig. 6).
Fig. 4. A comparison of consumptive water usage in the QQ and dry grind processes determined here and contrasted with previous surveys: (†) Keeney and Muller (2006). (The results were derived from a survey of average water use by ethanol plants located in Minnesota); (‡) Shapouri and Gallagher (2005). (The results were based on USDA 2002 survey); () Wu (2008). (The results were based on RFA 2007 survey, which indicated that the water consumption ranges from 2.65 to 4.9 L/L, with an average of 3.45 L/L).
991 Process water input (1.06 liter/liter)
Dryer loss (1.17 liter/liter) Corn (0.36 liter/liter)
QQ process
Saccharification (0.16 liter/liter) Germ (0.02 liter/liter)
Fiber DDGS (0.02 liter/liter) (0.04 liter/liter)
Fig. 5. The process water input–output analysis in the QQ process.
The savings of cooling water used for evaporator vapor recapture as well as distillation are the major contributors for the cooling water reduction in the QQ process. Due to the germ and fiber recovery at the front end, the amount of thin stillage loading to the evaporator in the QQ process is considerable lower than that in the dry grind process. Because the cooling water demand is proportional to the vapor loss rate, the lower loading rate of evaporator in the QQ process results in a lower vapor loss, and it thus leads to a lower cooling water demand to recapture the vapor loss. Approximately 0.42 L/L savings are achieved in the QQ process. The higher ethanol concentration facilitates the separation in the distillation system. Approximately 0.22 L/L of cooling water savings are achieved in the beer column of the QQ process. However, due to its lower loading rate, the savings in the rectifier column are lower than that in the beer column. Because ethanol concentrations are similar, the cooling water demands are comparable in the stripping column. To facilitate the yeast propagation, a cooler is set to cool the mash down to 30 ◦ C before feeding into the fermentation tank. The separation of germ and fiber at the front end reduces the materials processed to the cooler before fermentation, therefore providing 0.07 L/L savings of cooling water in the QQ process. 3.3. Product evaluation Regarding dry matter input, 100 kg of corn dry matter is fed into the process with 0.36 kg of urea. The total output of the QQ process is produced at the rate of 107.98 kg per 100 kg corn dry matter input. The difference between the input and output is due to the saccharification reaction where approximately 7.49 kg of process water are consumed for every 100 kg of dry corn.
Fig. 6. A cooling water demand comparison between the QQ and the conventional dry grind processes.
992
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993
Table 4 A coproduct quality comparison between the QQ and the conventional dry grind processes. QQ
Oil Protein NDF Starch Sugar Ash Other
Dry grind
Germ
Fiber
DDGS
DDGS
43.1% 15.4% 28.7% 7.6% 2.4% 2.6% 0.2%
8.9% 8.6% 69.9% 10.0% 0.9% 1.3% 0.5%
0.6% 46.0% 17.1% 7.4% 20.4% 4.2% 4.5%
12.9% 32.3% 32.1% 4.4% 12.1% 3.4% 2.8%
Ethanol and CO2 are the major products, each accounting for approximately one third of the total output. The remaining one third of the dry matter is incorporated in the solid coproducts: germ, fiber, and DDGS. Among these products, ethanol yield rate is the most important economic factor for an ethanol facility. Ethanol is produced at the rate of 0.405 L/kg of corn input (2.72 gal/bu) in the QQ process, which is lower than 0.414 L/kg (2.78 gal/bu) by the dry grind process. This is due to starch losses in the germ and fiber recovery process. After separation, the germ fraction contains 43% oil (d.b.) and can be further used for oil extraction; the fiber fraction is composed of 70% NDF (d.b.). In addition, a small amount of starch and sugars is recovered in both the germ and fiber streams. The starch accounts for 8% of germ fraction and 10% of fiber fraction, respectively. After germ and fiber recovery, all other nonfermantable materials along with some unconverted starch and sugar end in DDGS. Detailed compositions of germ, fiber, and DDGS streams are shown in Table 4.
Table 4 also compares the composition of DDGS produced in the dry grind and the QQ process. The prefractionation in the QQ process produces DDGS with higher protein and lower fiber content, thus making it an amenable feed for non-ruminant animals. In addition, the improved nutritional characteristics of DDGS may demand a premium price, further improving the economics of the ethanol facility if an optimistic market rewarding higher quality of DDGS comes to exist. 3.4. Economic performance 3.4.1. Baseline analysis An economic performance comparison between the dry grind plant and the QQ plant is given in Table 5. The comparison showed that the QQ plant requires a total capital investment of $96.2 million, 26.5% higher than that of the dry grind plant. The higher capital investment costs for the QQ plant are due to the additional germ and fiber recovery system. The annual raw material costs for the QQ plant are 20% higher than that for the dry grind plant, which corresponds to an increased processing capacity. Due to the lower ethanol yield rate, the QQ plant increases its unit raw material cost by 2.2%. Despite its increased processing capacity, the QQ plant demands less annual utility costs than the dry grind plant. Although the QQ process requires more electricity to power additional equipment, it provides significant savings in steam demand of distillation due to the germ and fiber recovery. The QQ plant reduces its utility costs for each gallon of ethanol production by 21.4%, which corresponds to findings in the energy demand analysis. The QQ plant requires higher annual operating costs due to its increased processing capacity. However, the unit operating costs for these two plants are comparable, both of which are approx-
Table 5 An economic comparison between a conventional dry grind plant and a QQ plant. Conventional dry-grind plant
QQ plant
$1000/yr
¢/L
¢/gal
¢/L
¢/gal
60,283 76,443
36.52 46.31
138.23 175.29
77,222 96,182
39.89 49.68
150.97 188.04
64,226 1500 190 474 63 1237 67,690
38.91 0.91 0.12 0.29 0.04 0.75 41.01
147.28 3.44 0.44 1.09 0.14 2.84 155.22
77,084 1790 234 532 70 1451 81,161
39.82 0.92 0.12 0.27 0.04 0.75 41.92
150.70 3.50 0.46 1.04 0.14 2.84 158.67
Utilities: Electricity Natural gas Steam Cooling water Subtotal
1989 2555 5521 181 10,246
1.21 1.55 3.34 0.11 6.21
4.56 5.86 12.66 0.42 23.49
2450 2579 4247 166 9442
1.27 1.33 2.19 0.09 4.88
4.79 5.04 8.30 0.32 18.46
Labor dependent Facility dependent Depreciation Operating costs
2898 2743 4019 87,596
1.76 1.66 2.43 53.07
6.65 6.29 9.22 200.86
5519 3514 5148 104,784
2.85 1.82 2.66 54.12
10.79 6.87 10.06 204.85
Products: Ethanol DDGS Corn germ Corn fiber Revenues
74,991 17,632 – – 92,623
45.43 10.68 – – 56.11
171.96 40.43 – – 212.39
87,956 11,809 13,141 2732 115,638
45.43 6.10 6.79 1.41 59.73
171.96 23.09 25.69 5.34 226.07
Gross profit (A) Taxes (40%) (B) Depreciation (C) Annual cash flow (A − B + C) Payback period (years)
5027 2011 4019 7035 10.9
3.05 1.22 2.43 4.26
11.53 4.61 9.22 16.13
10,854 4342 5148 11,661 8.3
5.61 2.24 2.66 6.02
21.22 8.49 10.06 22.80
Fixed capital costs Capital investment costs Raw materials: Corn Enzymes Yeasts Other chemicals Process water Denaturant Subtotal
$1000/yr
T. Lin et al. / Industrial Crops and Products 34 (2011) 986–993
993
Table 6 An economic comparison of the impact of investor ownership and loan requirements on cash flow in conventional dry grind and QQ processes. 12% dividends + 100% equity
12% dividends + 40% equity
Conventional dry grind
Conventional dry grind
$1000/yr
$1000/yr
¢/L
¢/gal
¢/L
¢/gal
¢/L
¢/gal
¢/L
¢/gal
Capital investment costs Equity Principle
76,443 76,443
46.31 46.31
175.29 175.29
96,182 96,182
49.68 49.68
188.04 188.04
76,443 30,577 45,866
46.31 18.52 27.79
175.29 70.12 105.17
96,182 38,473 57,709
49.68 19.87 29.81
188.04 75.21 112.82
Amortized payments Operating Costs Revenues
– 87,596 92,623
– 53.07 56.11
– 200.86 212.39
– 104,784 115,638
– 54.12 59.73
– 204.85 226.07
6030 93,626 92,623
3.65 56.72 56.11
13.83 214.69 212.39
7587 112,371 115,638
3.92 58.04 59.73
14.83 219.69 226.07
Gross Profit (A) Taxes (40%) (B) Depreciation (C)
5027 2011 4019
3.05 1.22 2.43
11.53 4.61 9.22
10,854 4342 5148
5.61 2.24 2.66
21.22 8.49 10.06
−1003 0 4019
−0.61 0.00 2.43
−2.30 0.00 9.22
3267 1307 5148
1.69 0.67 2.66
6.39 2.55 10.06
Dividends (D) Annual cash Flow (A − B + C − D) Payback period (years)
6115 920 83.1
3.70 0.56
14.02 2.11
7695 3966 24.3
3.97 2.05
15.04 7.75
2446 570 53.6
1.48 0.35
5.61 1.31
3078 4030 9.6
1.59 2.08
6.02 7.88
imately 54 ¢/L. This is due to the significant utility savings of the QQ plant, which offset higher depreciation and raw material costs. The coproducts provide a significant revenue boost for the QQ plant as compared to the dry grind plant. Although the revenue of DDGS in the QQ plant is reduced because of the prefractionation process, more value-added coproducts, such as germ and fiber, are produced. These factors taken together with the increased ethanol production cause the total revenues of the QQ plant to increase by almost $23 million. Assuming no external loans are required for both ethanol plants, the QQ plant has a higher annual cash flow. Despite its higher capital investment cost, the payback period of the QQ plant is reduced to 8.3 years, compared to 10.9 years for the dry grind plant. Increased processing capacity, more value-added coproducts, and reduced utility costs are three major factors boosting economic performance of the QQ plant. 3.4.2. Scenario analysis Table 6 outlines the results from two additional economic scenarios beyond the baseline case. These results suggest that the QQ facility will be considerably better in both scenarios. When investors seek outside funding, gross profits are reduced due to the additional amortized payments, while cash flow is increased because of the significant reduction of the dividends. The impact of seeking outside funding is particularly attractive for the QQ facility if the loan interest is 10% and dividend rate is 8%, where the payback period is reduced to 9.6 from 24.3 years. 4. Conclusion Comparing the QQ and the dry grind process models, the result shows that the QQ process reduces energy demand by 31.6% and water demand by 1.76 L/L, significantly as a result of its increased ethanol concentration in the beer. The QQ process produces more value-added coproducts, but has a lower ethanol yield rate (0.405 as compared to 0.414 L/kg). Because of its reduced utility costs and increased revenue by its value-added coproduct sell, the QQ process has a shorter payback period as compared to the conventional dry grind process. Acknowledgements The authors would like to thank Dr. Madhu Khanna and Dr. K.C. Ting for their help in preparing this work. This work was funded by
$1000/yr
$1000/yr
the Illinois Council on Food and Agricultural Research (C-FAR) and University of Illinois. Appendix A. Supplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.indcrop.2011.03.003. References Asano, T., Burton, F.L., Leverenz, H., Tsuchihashi, R., Tchobanoglous, G., 2007. Water Reuse: Issues Technologies, and Applications. McGraw-Hill, New York. Douglas, J.M., 1988. Conceptual design of chemical processes. McGraw-Hill, New York. Farrell, A.E., Plevin, R.J., Turner, B.T., Jones, A.D., O’Hare, M., Kammen, D.M., 2006. Ethanol can contribute to energy and environmental goals. Science 311 (5760), 506–508. Keeney, D. R., Muller, M. 2006. Water use by ethanol plants – potential Challenges. Available at: www.agobservatory.org/library.cfm?refid=89449. (accessed 10/ 20/ 2009). Kwiatkowski, J.R., McAloon, A.J., Taylor, F., Johnston, D.B., 2006. Modeling the process and costs of fuel ethanol production by the corn dry-grind process. Ind. Crop. Prod. 23 (3), 288–296. Li, C., Rodríguez, L.F., Khanna, M., Spaulding, A.D., Lin, T., Eckhoff, S.R., 2010. An engineering and economic evaluation of quick germ quick fiber process for dry-grind ethanol facilities: model description and documentation. Bioresour. Technol. 101 (14), 5275–5281. Pimentel, D., Patzek, T.W., Cecil, G., 2007. Ethanol production: energy, economic, and environmental losses. Rev. Environ. Contam. Toxicol. 189, 25–41. Ramirez, E.C., Johnston, D.B., McAloon, A.J., Yee, W., Singh, V., 2008. Engineering process and cost model for a conventional corn wet milling facility. Ind. Crop. Prod. 27 (1), 91–97. Rodríguez, L.F., Li, C., Khanna, M., Spaulding, A.D., Lin, T., Eckhoff, S.R., 2010. An engineering and economic evaluation of quick germ-quick fiber process for drygrind ethanol facilities: analysis. Bioresour. Technol. 101 (14), 5282–5289. Singh, V., Eckhoff, S.R., 1996. Effect of soak time, soak temperature, and lactic acid on germ recovery parameters. Cereal Chem. 73 (6), 716–720. Singh, V., Johnston, D.B., Naidu, K., Rausch, K.D., Belyea, R.L., Tumbleson, M.E., 2005. Comparison of modified dry-grind corn processes for fermentation characteristics and DDGS composition. Cereal Chem. 82 (2), 187–190. Singh, V., Moreau, R.A., Doner, L.W., Eckhoff, S.R., Hicks, K.B., 1999. Recovery of fiber in the corn dry-grind ethanol process: a feedstock for valuable coproducts. Cereal Chem. 76 (6), 868–872. Shapouri, H., Gallagher, P., 2005. USDA’s 2002 Ethanol Cost-of-Product Survey: Agricultural Economic Report Number 841. US Department of Agriculture, DC, Washington. Tiffany, D.G., Eidman, V.R., 2003. Factors associated with success of fuel ethanol producers. No. Staff Paper P03-7. In: College of Agriculture, Food, and Environmental Sciences. Department of Applied Economics, University of Minnesota. Watson, S.A., 1987. Structure and composition. In: Watson, S.A., Ramstad, P.E. (Eds.), Corn: Chemistry and technology. Am. Assoc. Cereal Chem., Inc., St. Paul, p. 69. Wu, M. 2008. Analysis of the efficiency of the U.S. ethanol industry 2007. Argonne, IL: Argonne National Laboratory. http://www.ethanol.org/pdf/ contentmgmt/2007 analysis of the efficiency of the us ethanol industry.pdf. (accessed 10/ 04/ 2010). Wu, M., Mintz, M., Wang, M., Arora, S., 2009. Consumptive Water Use in the Production of Ethanol and Petroleum Gasoline. Center for Transportation Research, Argonne National Laboratory ANL/ESD/09-1.