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Rosentrater, K. A. 2005. Expanding the role of systems modeling: considering byproduct generation from biofuel production. Ecology and Society 11(1): r2. [online] URL: http://www.ecologyandsociety.org/vol11/iss1/resp2/
Response to Chan et al. 2004. “The Role of Systems Modeling for Sustainable Development Policy Analysis: the Case of Bio-Ethanol.” Expanding the Role of Systems Modeling: Considering Byproduct Generation from Biofuel Production
1U.S. Department of Agriculture, Agricultural Research Service
The bioethanol industry has been experiencing rapid growth over the past several years, and is expected to continue to increase production for the foreseeable future. A vital component to the success of this industry is the sales and marketing of processing residues, which are primarily sold as dried distillers grains with solubles (DDGS). Systems modeling, a technique that has been used to predict future demand for bioethanol, can also be used to determine potential byproduct generation rates. This paper discusses the development of one such model, and presents predicted generation of DDGS as well as carbon dioxide emissions from this industry through 2100. These simulation results underscore the growing need to actively pursue research focused on value-added alternatives for the use of bioethanol byproduct streams.
Key words: bioethanol; biofuels; byproducts; carbon dioxide emissions; distillers grains; dynamic systems modeling; policy analysis; residue generation
With growing population, industrialization, and energy consumption, coupled with an increasing reliance on fossil fuels, the energy security needs of North America continue to escalate. Biofuels, renewable sources of energy, can help meet these increasing needs, and are produced from biomass sources including corn stover, residue straw, perennial grasses, legumes, and other agricultural and biological materials. At the moment, the most heavily used is corn. Fermentation of corn is readily accomplished at a relatively low cost vis-à-vis other biomass sources. However, in the coming years, the conversion of other lignocellulosic materials is expected to become cost competitive due to rapid technological advances (DePardo 2000).
Concern over resource inputs/outputs, economics, and impact of manufacturing and the use of bioethanol has led to many life cycle assessment studies (LCA). In addition to Chan et al. (2004), prominent studies include Andress (2002), Kaltschmitt et al. (1997), Kim and Dale (2002, 2004), Lynd and Wang (2004), Shapouri et al. (1995, 2002, 2003a,b), and Sheehan et al. (2002, 2004). Each manufacturing plant needs to contribute to the mission of sustainability in order for the entire system to succeed. A key element to this approach is to examine waste and byproduct streams (Rosentrater 2004). Modeling and simulation provides a tool for such analysis.
The primary objectives of this study are two-fold: 1) to further the discussion regarding the use of computer simulation to provide insights into bioethanol manufacturing, and 2) to expand the discussion to include processing residues. Based on Chan et al. (2004), a computer model was constructed to determine the quantity of byproducts resulting from bioethanol manufacture. This paper, therefore, raises the issue of the use and/or disposal of byproducts generated as this industry expands.
Although the bioethanol industry is poised to produce substantial quantities of biofuel during the next century, corn grain is currently the only biological material that can be economically converted into ethanol on an industrial scale. Thus, it is useful to examine this segment of the industry to establish a baseline for the consideration of bioethanol production from other potentially viable lignocellulosic materials.
Briefly, bioethanol manufacturing from corn grain results in three products: bioethanol, the primary end product; residual nonfermentable corn kernel components, which are typically marketed as “dried distillers grains with solubles,” known as “DDGS” (Fig. 1); and carbon dioxide (Fig. 2). Anecdotally, the rule of thumb commonly used in industry states that for 1 kg of corn processed, approximately 1/3 kg of each of the constituent product streams will be produced. The production process (Fig. 3) consists of several key steps, including grinding, cooking, liquefying, fermenting, and distilling the corn grain. Indepth information on this process, which is beyond the scope of this paper, can be found in Dien et al. (2003), Jaques et al. (2003), Tibelius (1996), and Weigel et al. (2005). Carbon dioxide results from the fermentation stage during starch consumption and metabolic conversion by yeast. This byproduct stream can be sold to specific compressed gas markets. Often, however, it is released to the atmosphere, because location or logistics prevent economic marketing. Distillers grains, on the other hand, are removed from the distillation stage, dried to ensure a substantial shelf life, and then sold to local livestock producers or shipped via truck or rail for use in distant livestock feed rations. Their sale contributes substantially to the economic viability of bioethanol manufacturing, and is vital to each plant’s operations. The quantity of processing residues that will be produced will substantially influence the future of the industry. Predictions of these can be accomplished via computer modeling and simulation.
Simulating byproduct generation
To predict the quantity of byproducts that may be produced during the next century by bioethanol manufacturing operations, a computer model was constructed using spreadsheet software. Several conversion factors were necessary to consider and incorporate, including: (1) Corn-to-bioethanol conversion (CEC): 2.58 kg corn/L bioethanol (Kim and Dale 2002); (2) Corn-to-byproducts conversion: 25.4 kg of corn typically produces approximately 7.98 kg of bioethanol, 7.71 kg of DDGS, and 8.35 kg of carbon dioxide (Kelsall and Lyons 2003). This equates to a corn-to-distillers grains conversion (CDGC) of 3.29 kg corn/kg DDGS, and a corn-to-carbon dioxide conversion (CCDC) of 3.04 kg corn/kg CO2; and (3) Energy density (ED) of bioethanol: 2.12 x 107 J/L (Sheehan et al. 2004).
To model the quantity of byproducts that may be produced during this time frame, i.e., 100 yr, several steps were involved. Briefly, key calculations included the rate of energy use (EU1; J/yr): information was based on predictions by Chan et al. (2004), Scenarios B and C.
Rate of ethanol use (EU2; L/yr):
Rate of corn use (CUR; kg/yr):
Dried distillers grains with solubles generation rate (DDGS; kg/yr):
Carbon dioxide generation rate (CD; kg/yr):
After programming, simulations were constructed based on energy use Scenarios B and C. However, the model can be used to simulate other use scenarios as well.
When constructing simulations, it is crucial to account for potential variations in numerical quantities and conversion factors, because these impact resulting calculated values. Literature reports a broad range of corn-to-distillers grains conversion, i.e., DDGS, rates from 0.282 to 0.323 kg DDGS/kg corn, as well as corn-to-carbon dioxide conversion, i.e., CCDC rates from 0.287 to 0.329 kg CO2/kg corn (Dien et al. 2003, Kim and Dale 2002, Lyons 2003, Shapouri et al. 1995, Tibelius 1996). These variations substantially affect the quantity of predicted byproducts that are determined by the computer model. Moreover, at individual manufacturing plants, variations in raw material inputs, equipment used, and operational procedures result in conversion rates that do not match values found in literature, but instead vary stochastically over both time and location. However, much of this information is proprietary and is not available in the literature. The computer model had to accommodate these variations, which thus led to differences in the calculated generation rates of the byproduct streams. Potential variations can most simply be accounted for by providing a range of potential conversion factors in the programming itself. In this study, ± 5% of the calculated conversion values were used to achieve this range.
Figure 4 predicts DDGS produced (± 5%) according to the quantity of manufactured bioethanol if Scenario B is realized; Fig. 5 depicts results for Scenario C. Figure 6 illustrates the carbon dioxide generated (± 5%) if Scenario B is realized; Fig. 7 depicts results for Scenario C. Not surprisingly, production rates of the byproducts increase in parallel with the quantity of bioethanol, as predicted by Chan et al. (2004). Most notably, the construction of this model has allowed specific quantities of byproducts to be predicted over time.
Recently, research has begun to address process modifications to reduce the amount of manufacturing byproducts. These include Barnes (2003), Johnston and Singh (2003), Murthy et al. (2004), Naidu and Singh (2003), Singh et al. (2001b), Singh et al. (2003), Singh et al. (2004), and Wahjudi et al. (2000). Figures 4 through 7, therefore, also illustrate production rates if these newer technologies can achieve 25, 50, and 75% reductions. If these modifications can be commercially implemented in production facilities, drastic reductions in generated byproducts could be realized. Thus, the success or failure of technological innovation has the potential for profound ecological ramifications as this industry continues to grow.
Regardless of potential new technologies, the overarching issue brought to light from these simulation results is the substantial increase in both distillers grains and carbon dioxide during the next century. The increased supply of distillers grains will affect the potential sales price vis-à-vis feed demand, whereas allowable carbon dioxide generation will be affected by greenhouse gas emission constraints. Both of these issues could severely affect the production economics of the industry in the near future. If estimates of future bioethanol production hold true, the current unidirectional approach may not be sustainable; thus, alternative avenues of use are necessary.
Currently, the bioethanol industry’s only outlet for the nonfermentable residues from the manufacturing process is livestock feeds. This approach is well established, but must be augmented if it is to retain its high-value returns because the generated quantities increase over time. Other novel uses such as human foods and industrial products should also be pursued.
Feeding dried distillers grains with solubles (DDGS) to animals is a viable method for the use of nonfermentable residues, because they contain high nutrient levels. Over the years, numerous studies have been conducted to optimize their use in livestock feed rations. Aines et al. (1986) and UM (2004) provide comprehensive reviews. Even so, much work remains to improve and maximize the use of these residues in animal feeds. Priorities should include:
Studies have also examined the possibility of using these streams in human food products. Some of the most recent include Abbott et al. (1991), Bookwalter et al. (1984), Brochetti et al. (1991), Kim et al. (1989), Maga and van Everen (1989), Rasco et al. (1990), van Everen et al. (1992), and Wall et al. (1984). To date, however, no commercial food products incorporate DDGS. In order for viable products to be successfully manufactured, additional research is needed. Studies essential to this effort include: (1) Analysis of current DDGS streams for food-grade applicability, especially nutritional contents and chemical levels, including vitamins, minerals, nucleic acids, pigments, heavy metals, and toxic compounds; and (2) Methods for processing DDGS into food grade ingredients, including:
Beyond these, little work to develop other value-added applications has been undertaken. Initial trials have been conducted investigating soil amendments and fertilizers (Erdem and Ok 2002, Ramana et al. 2002a,b), plastic composites (Julson et al. 2004), and extracting industrial chemicals (Kwiatkowski and Cheryan 2002, Singh and Cheryah 1998, Singh et al. 2001a, 2002). Other potential avenues do exist, and should be investigated, including:
Carbon Dioxide Emissions
Very little work has investigated the capture and use of carbon dioxide (Ginger 2004, Marland and Turhollow 1991). As demand for bioethanol increases, and more manufacturing plants are constructed and expanded, it will become essential to consider this issue, especially because the issue of greenhouse gas emissions continues to gain importance in environmental policies.
The bioethanol industry is poised to significantly contribute to meeting rising energy demands in coming years. Because this industry is not yet fully mature, questions have arisen regarding the nature of the industry itself. One concern is the quantity of byproducts that will be generated; thus, considering how these residues will ultimately be used is vital. Simulation modeling provides a tool that can be used to predict residue generation rates, and provide a baseline that can be used for discussions regarding use, as well as policy analyses for the bioethanol industry as a whole. Examining potential generation rates for the coming century underscores the pressing need for research and development into value-added uses for these materials if, given the current level of technology, the bioethanol industry is to remain cost competitive.
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ACKNOWLEDGMENTSThe author would like to acknowledge the helpful comments by the reviewers and editors.
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