Modeling the effects of value-added processing and logistics using ARENA

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1 Iowa State University From the SelectedWorks of Kurt A. Rosentrater March 29, 2008 Modeling the effects of value-added processing and logistics using ARENA Kurt A. Rosentrater, United States Department of Agriculture Elif Kongar, University of Bridgeport Available at:

2 MODELING THE EFFECTS OF VALUE-ADDED PROCESSING AND LOGISTICS USING ARENA Kurt A. Rosentrater, USDA, (605) , Elif Kongar, University of Bridgeport, (203) , ABSTRACT Scarcity and cost of nonrenewable fossil fuels have been a growing focus of many energy-dependent nations, including the U.S., for the past couple of decades. There are mainly two ways to approach these problems: (1) reducing the energy dependency and/or (2) developing alternative means of energy production. Utilizing biofuels, which are renewable sources of energy, is a promising way to produce energy. But, as this industry grows, so too does the quantity of byproducts, which are known as DDGS. This paper discusses a model that has been developed to analyze the economic viability of DDGS processing, and includes both deterministic and probabilistic variables. Capital and operational costs for various DDGS production rates and pelleting ratios are considered in the simulation, to validate the model. The developed model is run for a total of various scenarios including different production rates and pelleting ratios. Simulation results indicate that as pelleting ratio increases, the cost to ship DDGS by rail decreases substantially. These decreases are increasingly profound as DDGS production rate increases. Keywords: Energy, biofuels, byproducts, renewable energy, logistics, processing, simulation. INTRODUCTION The increasing cost of nonrenewable fossil fuels and potential decline in production in the future have been an increasing focus for many energy-dependent nations in recent years, including the U.S. There are two primary ways to approach these challenges: (1) reducing energy dependency, and/or (2) developing alternative methods of energy production. Utilizing biofuels, which are renewable sources of energy, is one of the promising alternatives for producing energy, and can be done in a relatively efficient manner, depending on the conversion technologies that are employed. There is a variety of biomass materials that can be used to produce biofuels, viz., residue straw, corn stover, perennial grasses, legumes, and other agricultural and biological materials. Currently, the most heavily utilized material in the U.S. is corn starch, since fuel ethanol production from corn can be accomplished very efficiently and at a relatively low cost compared to other biomass sources. In fact, corn starch is currently the only biological material that can be economically converted into ethanol on an industrial scale. The number of corn ethanol plants, and their processing capacities, has been exponentially increasing in recent years. The beginning of 2008 saw 136 manufacturing plants in the U.S. with an aggregate production capacity of 7.5 billion gal/yr (28.4 billion L/yr). In addition, 71 plants are currently under construction or expansion, and upon completion will contribute an additional 5.8 billion gal/yr (22.0

3 billion L/yr) [1,2]. Naturally, this growth in industry will have a direct impact on the amount of ethanol production, and thus the quantity of byproducts that are generated. In-depth details on ethanol manufacturing, which are beyond the scope of this paper, can be found in [3-8]. Briefly, there are mainly two techniques for producing fuel ethanol using corn grain: (1) wet mill processing and (2) dry grind processing. Wet mill processing tends to be highly capital intensive, whereas dry grind processing has significantly lower capital and operational requirements. Therefore, dry grind processing has rapidly gained prevalence in the industry. The dry grind production process (Figure 1a) consists of several key steps, including grinding, cooking, liquefying, saccharifying, fermenting, and distilling. Typically, ethanol is the primary end product of this process along with two byproducts: residual nonfermentable corn kernel components and carbon dioxide. Corn GRAIN STORAGE Grinding GRINDER MIXER Enzymes Slurry Tank COOKING TUBE VACUUM FLASH Enzymes Liquefaction Tank Saccharification & Propogation Enzymes MASH Enzymes COOLER Saccharification Tank Yeast Starter Tank Fermentation CO 2 FERMENTATION TANK Jet Cooker Cooking BACKSET CDS Liquefaction ETHANOL DENATURANT THIN STILLAGE EVAPORATOR SYRUP MOLECULAR SIEVE Centrate Centrifuge RECTIFIER STRIPPING COLUMN Distillation Centrifugation WHOLE STILLAGE BEER WELL DDGS DRYER DWG DDG DRYER Wet Cake Coproducts Processing Figure 1a. Process Flowchart for Dry Grind Fuel Ethanol Production. The nonfermentable kernel components (i.e., corn protein, fiber, and oil) are usually further processed (Figure 1b) and then marketed in the form of distillers dried grains with solubles (DDGS), and to a lesser degree in the form of distillers dried grains (DDG), which do not contain added solubles, distillers

4 wet grains (DWG), and condensed distillers solubles (CDS). Hereafter distillers grains will be used in a generic sense to refer to all of these byproduct materials. Stripper Column Backset (10 50% of total thin stillage) Whole Stillage 5 15% solids Evaporator Centrifuge Wet Cake 35 50% solids Thin Stillage (centrate) 5 10% solids Syrup (CDS) 25 55% solids Mixer Dryer Dryer Dryer DWG DDG DWGS DDGS CDS Dried Solubles Figure 1b. Process Flowchart for Ethanol Byproduct Processing. Residue streams are separated from the ethanol during distillation. They are often dried to approximately 10% moisture content, to ensure a long shelf life and facilitate transportation, and are then sold as distillers grains (most often DDGS or DDG) to local livestock producers or shipped via rail to livestock feed markets (Figure 2) throughout North America, and they are increasingly being exported to overseas destinations as well. Dryer Storage Loadout Figure 2. Simplified Flowchart of Current DDGS Processing Steps that Could be Augmented by Value- Added Processing. The sale of distillers grains contributes substantially to the economic viability of ethanol manufacturing (generally between 10 and 40% of an ethanol plant s entire revenue stream, depending upon DDGS sales price and other market conditions), and is thus a vital component to each plant s operations. Because of the dynamics of the free market economy under which this industry operates, the quantity of processing residues that will be produced as this industry continues to grow (and the ability to utilize them) will significantly impact the future of the industry.

5 A persistent barrier to effective utilization of distillers grains is product storability and flowability. DDGS is a granular, powder-like material that consists of a range of particle sizes and shapes. DDGS is typically shipped in trains and trucks throughout the U.S. to be used for animal feed. However, DDGS is often difficult to unload once the vessel reaches destination, because the particles lock together and have flowability problems. This necessitates manual unloading processes, which creates a substantial financial burden for the ethanol manufacturer. This flowability problem is resulting in serious economic implications for ethanol plants (e.g., sledgehammers, shovels, and pick axes must be used to get the DDGS to discharge). These economic losses include rail car repairs (due to damage), and labor expenses. Another issue with the transportation of DDGS is the weight vs. volume of each car. Railcars containing DDGS are filled to volumetric capacity for shipping, but are often not at maximum allowable weight, due to the low bulk density of the granular material itself (approximately 30 lb/ft 3 [480 kg/m 3 ]), thus causing additional potential economic loss to the ethanol manufacturer. Thus there exists a serious need to modify the DDGS that is currently produced in the industry (and inherently the processes which are used to produce the DDGS), or to alter the storage and transportation systems for these products (which could be a very expensive proposition). Pelleting is a manufacturing process that is commonly used to densify granular materials. This process is both feasible and appropriate for DDGS, and can be accomplished using conventional feed milling equipment [9]. In fact, pelleting of DDGS (Figure 3) would simultaneously overcome the flowability problem, as well as allow shipping vessels to be filled to both weight and volumetric capacity. Ultimately, however, the adoption of this technology is contingent upon price ramifications to the ethanol plant, which has not yet been thoroughly explored. Cost-effectiveness of pelleting DDGS versus current industrial practice, which is to ship DDGS as-is (i.e., in granular bulk form) has to be investigated. Rosentrater (2007) [9] has proven the feasibility of this type of processing, and has shown that DDGS can be pelleted. Pellet Dryer Storage process Loadout Item Description 301 Conveyor 302 Bucket Elevator 303 Scale / Surge Bin 304 Vibrator 305 Gate 306 Screw Conveyor 307 Conditioner 308 Pellet Mill 309 Dryer / Cooler 310 Conveyor 311 Bucket Elevator 312 Scale / Surge Bin 313 Gate Figure 3. Proposed Flowchart for DDGS Processing Augmented by Pelleting.

6 The question that remains, however, regards the cost of this additional processing step. In order to answer this question, a computer model was developed and implemented to examine the economic implications of pelleting DDGS for varying production and pelleting rates with the help of simulation models. The computer models were developed using Arena v CPR 7 (Rockwell Software, Inc.). 2. DETERMINISTIC AND STOCHASTIC SIMULATION MODELS FOR DDGS PELLETING In order to investigate the economic implications of pelleting DDGS, two separate computer models were developed. The first simulation model employed only deterministic variables, whereas the second model embodied stochastic variables to take possible dynamic variations into account. The deterministic model discussed in this paper was previously described by Rosentrater and Kongar [14]. The following discussion briefly explains the algorithms that were used for both the deterministic and stochastic simulation models Algorithm Development Both dependent and independent variables were embedded in the simulation models as follows. These were based on data available for a range of current ethanol processing facilities. i. Data for daily DDGS generation, g, (tons/day) are given as in (1): g = 0, 100, 200, 300, 400, 500, 600, 700, 800, 900, (1) Data for the percentage of DDGS pelleted (%) are given as in (2): p = 0, 25, 50, 75, 100. (2) ii. Bulk density of p percent pelleted DDGS (tons/ft 3 ), d p, can be defined as in (3.0) for the deterministic model: d 0 = 30 lb/ft 3 = tons/ft 3 d 25 = 32.5 lb/ft 3 = tons/ft 3 d 50 = 35 lb/ft 3 = tons/ft 3 (3.1) d 75 = 37.5 lb/ft 3 = tons/ft 3 d 100 = 40 lb/ft 3 = tons/ft 3. As for the stochastic model, it is estimated that the bulk density will vary by +/- 5% of above stated values (each level of pelleting has its own d p value), which will be distributed normally. Hence, d o = N(30,0.01) lb/ft 3 d 25 = N(32.5,0.01) lb/ft 3 d 50 = N(35,0.01) lb/ft 3 d 75 = N(37.5,0.01) lb/ft 3 d 100 = N(40,0.01) lb/ft 3, Where, µ,δ is the absolute value of the normally distributed variable with parameters µ and δ. iii. Pelleted quantity, pq, (ton/day) can be calculated as in (4): (3.2)

7 pq = g*p. (4) The total operating cost of pellet processing can be given as in Table 1 for the deterministic model. Table 1. Total Operating Cost of Pelleting Process for the Deterministic Simulation Model p (tons) Co p ($/ton) p (tons) Co p ($/ton) As for the stochastic model, the values for Co p are estimated to vary by +/- 5% of the values stated in Table 1 (i.e., each level of pelleting has its own Co p value, as shown in Table 2), and will be distributed normally. Table 2. Total Operating Cost of Pelleting Process for the Stochastic Simulation Model p (tons) Co p ($/ton) p (tons) Co p ($/ton) 100 N(1.4700,0.05) 600 N(1.1025,0.05) 200 N(1.3965,0.05) 700 N(1.0290,0.05) 300 N(1.3230,0.05) 800 N(0.9555,0.05) 400 N(1.2495,0.05) 900 N(0.8820,0.05) 500 N(1.1760,0.05) 1000 N(0.8085,0.05) iv. Transportation costs Transportation variables, along with their corresponding descriptions and values, are provided in Table 3 for the deterministic model. Table 3. Shipping Cost Data for the Deterministic Simulation Model Variable Description Value x DDGS shipping cost per rail car $4,800/car y Maximum volumetric capacity per rail car 6,350 ft 3 /car wt Maximum theoretical weight per rail car 109 ton/car s DDGS sales price $120/ton t Train capacity 10,447.5 tons Maximum cars/unit train 96

8 As for the stochastic model, it is estimated that the DDGS shipping cost per rail car (x) will follow a triangular distribution with a minimum value of 4,000 $/car, maximum value of 5,000, and a most likely value of 6,000. In addition, DDGS sales price is also assumed to follow a triangular distribution with a minimum value of 61.6 $/ton, maximum value of 131, and a most likely value of 81 (Table 4). These parameters were obtained from the historical DDGS sales price data depicted in Figure 4. Table 4. Shipping Cost Data for the Stochastic Simulation Model Variable Description Value x DDGS shipping cost per rail car TRI(4000;5000;6000) $/car y Maximum volumetric capacity per rail car 6,350 ft 3 /car wt Maximum theoretical weight per rail car 109 ton/car s DDGS sales price $120/ton t Train capacity 10,447.5 tons Maximum cars/unit train 96 Figure 4. Monthly Data for the DDGS Price Between October 2000 and October 2007 [15]. Depending on the data provided in Table 3 and Table 4, the actual car weight when filled according to per percent pelleted DDGS in (ton/car) can be expressed mathematically as in (5). wa p = y*d p. (5) Theoretical shipping cost of per percent pelleted DDGS in ($/ton) can be given as: Ct p = x/wt, (6)

9 where, actual shipping cost of per percent pelleted DDGS in ($/ton) is: Hence, slack (unused) capacity in ($/ton) can be calculated as: Depending on Eq. (8) total slack cost per car can be calculated as in (9). Ca p = x/wa p. (7) w p = Ca p - Ct p (8) SC car = s(wt wa p ) + w p (wt wa p ) (9) Where w p is the shipping expense for unused space in the rail car. Pelleting cost per car (P car ) and net cost per car (N car ) be calculated as in (10) and (11): P car = (Co p wa p.p/100), (10) N car = SC car + P car. (11) Theoretical number of cars can be given as: rt = t/wt, (12) where function. returns the nearest larger integer. Actual number of cars can be expressed mathematically as: ra = t/wa p. (13) Theoretical leasing fee (lt) and actual leasing fee (la) can be given as in (14) and (15): lt = rt*x, (14) la = ra*x. (15) Therefore, the rail car leasing slack value in ($/train) is: ls = la lt (16) Depending on above equations, the total pelleting cost (P) in ($/train) and the net cost impact (N) in ($/train) can be given as below: P = ra * P car, (17) N = P + ls. (18) The number of trains required per month can then be calculated as: nt = 1(t/g)*30, (19)

10 whereas, the net cost impact (NCI) to the ethanol plant can be expressed mathematically in ($/month) as in (19): NCI = N*nt. (20) RESULTS AND DISCUSSIONS Results for the net cost per car to ship DDGS (Figure 5.1.a) indicate that, compared to the baseline case (i.e., shipping DDGS as-is, with no pelleting), all scenarios that involve pelleting have reduced shipping costs. And, the least shipping costs are produced when 100% of the DDGS is pelleted. As DDGS generation rate increases, a slight decrease in shipping cost is observed (due to economies of scale). On the other hand, when the fraction pelleted increases, large decreases in shipping costs are observed (due to the weight versus capacity for each car). For example, at a DDGS generation rate of 100 tons/day, 0% pelleted DDGS had a shipping cost 19.8 times greater than 100% pelleted DDGS. At 1,000 tons/day, the 0% pelleted had a cost 36.0 times greater than 100% pelleted. Results for the net cost per train to ship DDGS (Figure 5.2.a) once again indicate that, compared to the baseline case (i.e., no pelleting), all scenarios that involve pelleting have reduced shipping costs. The least shipping costs are produced when 100% of the DDGS is pelleted. As DDGS generation rate increases, a slight decrease in shipping cost is observed (due to economies of scale). On the other hand, when the fraction pelleted increases, large decreases in shipping costs are observed. For example, at a DDGS generation rate of 100 tons/day, 0% pelleted DDGS had a shipping cost 8.4 times greater than 100% pelleted DDGS. At 1,000 tons/day, though, the 0% pelleted had a cost 15.3 times greater than 100% pelleted. As expected due to the previous results [14], the net cost impact per month to ship DDGS (Figure 5.3.a) indicates that, compared to the baseline case (i.e., no pelleting), all scenarios that involve pelleting have reduced shipping costs. And, the least shipping costs are produced when 100% of the DDGS is pelleted. As both DDGS generation rate and the fraction pelleted increase, large increases in monthly cost impact are produced (because of overall decreases in shipping cost as the fraction pelleted increases, which is due to both economies of scale as well as weight versus capacity for each car). For example, at a DDGS generation rate of 100 tons/day, 0% pelleted DDGS had a shipping cost 8.4 times greater than 100% pelleted DDGS. At 1,000 tons/day, the 0% pelleted had a cost 15.3 times greater than 100% pelleted. As for the stochastic results, the baseline case always seems to be dominated by pelleting options. In addition, the introduction of stochastic variables seem to decrease the net cost per car to ship (4.2.a). However, for both net cost per train to ship and the net cost impact per month to ship DDGS stochastic variables seem to significantly increase the overall cost values compared to the deterministic results. These results are probably more reflective of real life, as they more realistically depict the dynamic market place in which the fuel ethanol industry operates. The next step for this line of research should include model verification and validation.

11 Figure 5. Deterministic vs. Stochastic Simulation Results.

12 CONCLUSIONS In this paper both deterministic and stochastic simulation models are presented to investigate the economics of a proposed pelleting process for DDGS. The focus of this work was to compare two methods of utilizing ethanol byproducts: (1) value-added processing of DDGS by pelleting, and (2) current industrial practice, which is to ship DDGS as-is (i.e., in granular bulk form). The simulation results indicate that pelleting is indeed cost-effective for these manufacturing residues. In terms of cost/car and cost/train, pelleting has been shown to reduce costs for shipping DDGS for all production rates studied the greater the fraction pelleted, the greater the cost savings. In terms of cost/month to an ethanol plant, it appears that both production rate and fraction pelleted have considerable effects, all of which point toward a greater cost savings when the DDGS is completely pelleted prior to shipping. REFERENCES [1] BBI, U.S. Production Capacity. Existing Plants. BBI International. Available online: [Accessed 08 January 2008]. [2] RFA, Ethanol industry outlook Washington, D.C.: Renewable Fuels Association. Available online: [Accessed 08 January 2008]. [3] Bothast, R.J. and Schlicher, M.A., Biotechnological processes for conversion of corn into ethanol. Applied Microbiology and Biotechnology 67: [4] Dien, B.S., Bothast, R.J., Nichols, N.N., Cotta M.A., The U.S. corn ethanol industry: an overview of current technology and future prospects, in: The Third International Starch Technology Conference Coproducts Program Proceedings, eds. M. Tumbleson, V. Singh, and K. Rausch, 2-4 June, 2003, University of Illinois, pp [5] Jaques, K.A., Lyons, T.P., Kelsall D.R., The Alcohol Textbook. Nottingham University Press: Nottingham, UK. [6] Maisch, W.F., Fermentation processes and products, in: Corn Chemistry and Technology, 2nd ed. White, P.J. and Johnson, L.A. eds. American Association of Cereal Chemists: St Paul, MN. [7] Tibelius, C., Coproducts and Near Coproducts of Fuel Ethanol Fermentation from Grain. Agriculture and Agri-Food Canada Canadian Green Plan Ethanol Program: Starchy Waste Streams Evaluation Project. Available online: [Accessed 07 January 2007]. [8] Weigel, J.C., Loy, D., and Kilmer, L., Feed Co-Products of the Dry Corn Milling Process. Iowa State University and Iowa Corn Promotion Board. Available online: [Accessed 07 January 2007]. [9] Rausch, K.D., Belyea, R.L., The future of coproducts from corn processing. Applied Biochemistry and Biotechnology 128, [10] Rosentrater, K.A., Giglio, M., What are the challenges and opportunities for utilizing distillers grains? Distillers Grains Quarterly 1(1), [11] Rosentrater, K.A., Expanding the role of systems modeling: considering byproduct generation from biofuel production. Ecology and Society 11(1), 1-12.

13 [12] UMN, The Value and Use of Distillers Dried Grains with Solubles (DDGS) in Livestock and Poultry Feeds. University of Minnesota. Available online: [Accessed 08 January 2008]. [13] Rosentrater, K. A., Can you really pellet distillers grains? Distillers Grains Quarterly 3(3): [14] Rosentrater, K., Kongar, E., Biofuel Coproducts: Modeling the Effects of Value-Added Processing and Logistics, 5th International Logistics and Supply Chain Congress 2007, November 7-9, Istanbul, Turkey. [15] USDA ERS, Feed Grains Database: Yearbook Tables. United States Department of Agriculture, Economic Research Service. Available online: [Accessed 07 January 2008].