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Keywords:

  • Carbon-to-liquid fuels;
  • Corn ethanol;
  • Thermochemical conversion

Abstract

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES

The recent investment boom and collapse of the corn ethanol industry calls into question the long-term sustainability of traditional approaches to biofuel technologies. Compared with petroleum-based transportation fuels, biofuel production systems are more closely connected to complex and variable natural systems. Especially as biofeedstock production itself becomes more independent of fossil fuel–based supports, stochasticity will become an increasingly important, inherent feature of biofuel feedstock production systems. Accordingly, a fundamental change in design philosophy is necessary to ensure the long-term viability of the biofuels industry. To respond effectively to unexpected disruptions, the new approach will require systems to be designed for resilience (indicated by diversity, efficiency, cohesion, and adaptability) rather than more narrowly defined measures of efficiency. This paper addresses important concepts in the design of coupled engineering-ecological systems (resistance, resilience, adaptability, and transformability) and examines biofuel conversion technologies from a resilience perspective. Conversion technologies that can accommodate multiple feedstocks and final products are suggested to enhance the diversity and flexibility of the entire industry. Integr Environ Assess Manag 2011;7:348–359. © 2011 SETAC


INTRODUCTION

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES

In the aftermath of Hurricane Katrina, adjustments in international oil markets, US agricultural (corn and soybean) prices, and policy incentives coincided with a massive investment boom in corn ethanol production (Schnepf and Chite 2005). Corn ethanol production in particular more than tripled between 2002 and 2007, reaching 6.5 billion gallons in 2007—accounting for more than 90% of all the biofuels produced in the United States and displacing 4.5% of nation's annual gasoline use (USEIA 2007; RFA 2010). However, the economic incentives that sparked the corn ethanol boom proved to be temporary. Despite a combination of government subsidies, tax incentives, consumption mandates (Krauss 2009) and efficiency improvements, the long-term economic viability of the corn-based ethanol industry has been called into question by the recent bankruptcy of one of the world's largest producers (Daily Finance 2008). Moreover, current biofuel production technologies have become problematic from social and environmental perspectives. In particular, concerns with regard to food prices (Food & Water Watch et al. 2007), indirect greenhouse gas (GHG) emissions (Fargione et al. 2008; Searchinger et al. 2008), and water use (Aden 2007) have undermined support for policies favoring corn ethanol production. This sudden change from hurried expansion to abrupt financial failure focuses attention on the sustainability (in economic, social, and environmental terms) of biofuel industries and provokes questioning of conventional design approaches for the biofuel production systems, especially with regard to the optimization approach that is typical of petroleum-based production industries.

Compared with conventional, fossil-based transportation fuel production (Figure 1), current biofuel industries are much more closely connected to and heavily influenced by natural systems that are essentially complex, dynamic, and full of variability and even unpredictability (Holling and Gunderson 2002). This relationship is particularly true for the feedstock production (agriculture) stage of biofuel production. Normally, the interaction and interdependence of biofuel industries and natural systems takes place at multiple spatial and temporal scales. Ultimately, the pressures placed on natural systems by industrial production feed back to the industrial system within or across the original system scale (Fiksel 2006; Seager, 2008). For example, the intensive corn planting required for ethanol manufacturing may consume a great volume of water, leading to depletion and degradation of local and regional freshwater resources (aquifers, streams, and lakes), which may in turn create a scarcity of freshwater supplies that constrain future ethanol production.

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Figure 1. Comparison of current biofuel production with traditional transportation fuel production (degree of complexity and predictability).

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One result of the close coupling of natural systems and industrial systems is increased complexity and reduced predictability. (Figure 1 shows the gradually increased complexity and unpredictability of systems in the life cycle of biofuel production.) Compared with fossil fuel–based systems, biofuel production systems can be expected to behave more like a complex adaptive system that cannot be predicted by understanding a single process, factory, or system in isolation. The industrial components of a biofuel production system must eventually encounter unpredictable changes and disruptions from natural systems. This interaction is anathema to a traditional, optimization-based approach to biofuel system design. Prevailing engineering design practices based on deterministic assumptions that are operable within a narrow range of conditions and scales may be a prescription for failure in closely coupled bioindustrial systems. Once a biofuel system is constructed for the optimal state, this design constrains the operating parameters for the entire service life of the system. However, large enough external disturbances could drive the system away from the optimal state, even leading to the collapse of the entire system. Moreover, because those disturbances can be difficult to predict, it is not possible for designers to anticipate all future scenarios in the initial design. Therefore, a biofuel system designed in an optimization approach is especially vulnerable to unforeseen changes, despite resistance to some predictable disruptions. For example, since the corn ethanol construction boom following Hurricanes Katrina and Rita, there has been a dramatic fall in transportation fuel prices (USEIA 2009), while the price of corn grain has stayed comparatively high (USDA 2010). This combination drove corn ethanol margins down to the point where even major producers with new, efficient dry-mill plants, were forced into bankruptcy (e.g., Biofuels Journal 2008).

Unfortunately, most research on biofuels has focused on efficiency, especially energy (Bothast and Schlicher 2005; Singh et al. 2005), financial (McAloon et al. 2000; Aden et al. 2002), or environmental efficiency (Pimentel 2003; Farrell et al. 2006; Wang et al. 2007). There is little understanding of the capacity of biofuels industries to respond to rapid, nonlinear, and unpredictable changes and exogenous shocks, such as technology changes, market dislocations, societal changes, or environmental impacts. This lack of knowledge arises mainly from the typical engineering approach to biorefineries, which is to transfer skills learned in the petroleum industry (such as chemical process optimization). However, for long-term sustainability, biofuel systems must be designed in a fundamentally different way than traditional transportation fuel production systems.

Given the close connection between biofuel production systems and ecosystems, it is natural to search the knowledge base of ecology for new approaches. Rather than emphasizing an optimization approach typical of petroleum industries, it may be sensible for biofuel systems to consider resilience, which describes the capacity of an ecological system to maintain or recover basic functions relative to changing conditions, damages, or perturbation (Holling 1996). Resilience has recently been discussed in an industrial engineering context (Fiksel 2003; Hollnagel et al. 2006) and suggested as a way of resolving problems with regard to unpredictability of complex systems. Some qualitative measures have been developed to provide practical strategies to improve resilience (Fiksel 2003). However, there is still a paucity of case studies that apply resilience thinking in biofuel industries and a lack of understanding of the informative power or limitations of resilience. Moreover, transformability, an attribute describing how ecosystems can be fundamentally altered by external and internal changes, has never been brought into the context of industrial systems.

This paper addresses the long-term sustainability of the biofuel production industry by incorporating resilience thinking into system design and management. Important attributes in resilience thinking—resistance, resilience, adaptability, and transformability—are described in the context of ecosystems, then for biofuel industries, at multiple temporal and spatial scales. Also, different technologies for biofuel production are examined from a resilience perspective. Some principles in process selection and integration are proposed in the design of a biorefinery. A flexible-feedstock, carbon-to-liquid fuel (FCTL) process is proposed to show how resilience thinking could be integrated into biofuel production design. Through applying resilience thinking into process design, biofuel production would be adaptable to a myriad of physical, geographic, and economic conditions, adaptive to generate multiple outputs, mutually symbiotic with a variety of industry sectors, environmentally efficient, and readily scalable. Resilience thinking could also guide policy makers in regulating biofeedstock production, biofuel conversion, and waste disposal or beneficial reuse from biofuel production. In addition, principles proposed for biofuels can be applied to other industrial systems for long-term sustainability.

ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES

The concept of resilience was first proposed by Holling (1973) to explain key features of ecological systems. Later, resilience was extended to managed ecosystems known as social-ecological systems (SESs; Holling 1996; Carpenter et al. 2001; Walker et al. 2004). Resilience is an intrinsic property of complex systems that describes a capacity to deal with changes and disturbances while maintaining fundamental structure and functions (Holling 1996; Walker and Salt 2006). Because the focus here is on how systems respond to external stress, it is necessary to understand system behavior and dynamics with respect to resilience.

In the context of resilience, ecosystems have sometimes been described as “stability landscapes” within basins of attraction (Figure 2). Using a thermodynamic analogy, the stability landscape is a collection of system states that are defined by system variables and processes. A basin of attraction is a region in system-states space to which the system will naturally tend. The bottom of the basin is the optimal state of the ecosystem, representing the lowest “potential energy” at which the system maintains order (Walker et al. 2004; Walker 2005; Ostasiewicz et al. 2008). The ball in the basin represents the current system state. The system tends to move toward the bottom of the basin, but the external disruptions may push the current system state (the ball) away from the bottom toward a threshold (the edge of the basin). If the system crosses the threshold and moves to other landscapes, the existing system will lose its identity and integrity (manifested as loss of structure and functionality). Because the combined effects of “attraction” and “disruption” balance each other, at most times an ecosystem operates at a steady or pseudosteady state far away from the bottom of the basin (Holling 1973; Holling 1996; Holling and Gunderson 2002).

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Figure 2. System dynamics (explained by 2-dimensional stability landscape with basins of attraction).

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In Figure 2, resilience corresponds to the capacity to keep the system in the existing basin without crossing the threshold to another basin. Industrial systems, SESs, and ecological systems can all be classified into 3 types (Figure 2A). Engineered systems are typically resistant to change and therefore characterized by steep sides that speed return of perturbed engineered systems to a single steady state (Pimm 1984). A resistant system often addresses a single operating objective (e.g., efficiency) to resist failure under states within a narrow range (Ostasiewicz et al. 2008). Many conventional industrial systems fall into this category. Systems with ecosystem resilience emphasize the width of the basin as well as the depth. Ecosystem resilience is measured by the magnitude of disturbance absorbed before systemic collapse (Holling 1996). A system with ecosystem resilience can function across a broad spectrum of possible states and return gradually to steady state. It can survive larger perturbations than resistant systems, although the system operates far from equilibrium most of the time. Lastly, some systems may have multiple basins of attraction. These systems shift to another basin when facing large disruptions (Fiksel 2003; Zhao et al. 2008; Gunderson 2000; Holling 1973; Holling and Gunderson 2002). Compared with ecologically resilient systems, systems with multiple equilibrium states may have higher average resource efficiency and stay closer to an equilibrium state longer than systems with simpler ecological resilience.

In ecology, the capacity to manage resilience is called adaptability (Walker et al. 2004). It is the capacity to trigger a system adaption, manifesting as a collection of smooth and slow changes of structure and functionality, to enhance or reduce resilience. Ecosystem resilience can change through self-organization or through active management from humans (Walker et al. 2004). For ecosystems, increasing species that provide diverse functions could add both depth and width of a basin (Figure 2A), thus increasing resilience (Peterson et al. 1998). Shrinking basins of attraction can erode resilience, which moves the system to another basin in the same landscape (Figure 2B-1) or causes it to cross its threshold and move to an entirely different basin (Figure 2B-2). Adaptability is especially important for a system that is in an undesirable basin (a basin with fewer ecosystem services from a human perspective). For example, a shallow lake generally has 2 basins of attraction: a desirable system is characterized as clear water and an undesirable basin as turbid water (Carpenter et al. 2003). Because of fertilizer application in agriculture, huge amounts of phosphorus can enter the lake, causing the lake system to fall into the turbid water basin with frequent blooms of toxic algae. Reducing P use in agriculture can help the system shrink the undesirable basin and push the system to return to the original basin (the situation in Figure 2B-1). Some authors consider adaptability one of the principal determinants of resilience (Folke et al. 2002).

The capacity to create or move to a fundamentally new system (described as a new landscape) is called transformability (Walker et al. 2004). The new system has to be defined in part or overall by new state variables and processes. System transformation is demonstrated by a sudden and radical change of essential structure and function. When the existing system is locked in an undesirable landscape, it is necessary to create a new desirable landscape and move the system into it. To trigger a system transformation, components (such as foreign species) can be directly introduced to or removed from existing systems (Walker et al. 2004). For example, a rangeland system may be locked in an undesirable basin with little grass, many shrubs, and few livestock after decades of cattle ranching. Introducing tourism and hunting to the land could create a new landscape, such as one based on wildlife conservation. Undesirable systems (such as eutrophied lakes) may nevertheless be resilient. Intentionally undermining the resilience of an unhealthy system (e.g., by reducing nutrient levels or removing invasive species) may allow release of resources that support system transformation (as depicted in Figure 2B-2).

RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES

In practice, it is difficult to measure resilience, adaptability, and transformability based on system regimes and landscapes. Especially for those systems defined by multiple variables, calculating the size of the basin of attraction may require extraordinary quantities of data, which are often not available (Carpenter et al. 2001; Ostasiewicz et al. 2008). Nonetheless, Fiksel (2003) constructs a group of concepts to provide a practical understanding of resilience in both ecological and industrial systems. Four major characteristics are identified and explained: diversity, efficiency, cohesion, and adaptability (Table 1). Enhancing those characteristics improves resilience. For example, increasing species diversity provides functional redundancy at multiple scales, which could maintain ecosystem services and feedbacks despite sudden loss of some species (Peterson et al. 1998; Folke et al. 2004). When applied to industrial systems, diversity implies a wide range of alternatives, such as multiple products, configurations, or sites (Seager and Korhonen 2008). In biofuel production systems, diversity could come in the form of different feedstocks, process technologies, and coproducts, which could buffer the plant from external price or supply shocks.

Table 1. Characteristics of resilience and transformability (Fiksel 2003; Seager, 2008)
 CHARACTERISTICS OF RESILIENCETransformability
DiversityEfficiencyCohesionAdaptability
DefinitionExistence of multiple forms and behaviorsPerformance with modest resource consumptionExistence of unifying forces or linkagesFlexibility to change in response to new pressuresCapacity to create a fundamentally new system
Ecological SystemBiological diversity at multiple scalesEfficient ecological cycling of energy and nutrientsMutualismAbility to create multiple equilibrium statesChange of dominant species
Industrial SystemMultiple products, services; alternative sitesCost-efficiency; eco-efficiencyStrong brand identity; unique product features; linkages to other industriesAbility to change practices, design, and resource allocationReplace bike industry with car industry
Biofuels SystemDiverse feedstock, coproducts, technologies; Distributed sitesImprovement on cost, ecological and energy efficienciesHigh-quality products; beneficial reuse of wastes; increases linkage to other systemsFlexible feedstock; flexible products; flexible conversion processesBiofuels plants convert to produce food, pharmaceuticals, or plastics

Fiksel's (2003) description of resilience addresses both engineering resilience (e.g., efficiency) and ecosystem resilience (e.g., diversity, adaptability, and cohesion). Traditional measures of economic efficiency (e.g., costs and benefits) are necessary because only industries with net income can survive in a competitive market. However, efficiency is often in opposition to the other 3 characteristics. Even improvements in eco-efficiency, which reduce costs and environmental impacts, rarely result in improved diversity and flexibility (Seager and Korhonen 2008), because improvements in eco-efficiency may still be too narrowly directed. For example, switching from wet-mill to dry-mill processes for ethanol production improves yield and reduces cost, but at the same time produces fewer coproducts. From an efficiency perspective, dry mills produce more ethanol per unit of grain input. However, they also reduce diversity (of coproducts), adaptability (through reduced flexibility in product mix), and cohesion (by supporting fewer end markets). Additionally, improvement of diversity, adaptability, and cohesion may increase complexity and uncertainty, which is generally perceived as a negative in industrial systems. For example, using solar or wind energy could diversify the resources of current energy systems, but it will also increase the complexity of energy systems with respect to energy extraction, storage, and distribution, thereby necessitating improvements to the electrical grid. Similarly, if a biofuel plant is designed to use multiple feedstocks, more energy, chemicals, and investment may be required in pretreatment, depending on the feedstock characteristics and technological requirements (Mosier et al. 2005). This design will reduce overall energy efficiency of biofuel production and increase cost but improve diversity (through increased variety in feedstocks) and cohesion (by accepting biomass residuals from several industries). These trade-offs have to be considered when designing a biofuel system for resilience.

Compared with resilience, almost no effort has been made to see whether and how transformability can be integrated into the design of industrial systems, mainly because transformability of industrial systems is highly disruptive to the economic status quo. Consequently, political or social factors may resist abrupt changes of current industrial systems. Moreover, industrial systems are typically designed for resistance, making them hard to transform. Sometimes, an industrial system can be “locked in” to an undesirable or suboptimal state that has high social costs. In this case, “creative destruction” (Schumpeter 1950) in the form of innovation is called for. This process initiates the release and reorganization of resources (Seager, 2008). System transformation is inevitable in the life cycle of system development. Biofuels may be merely a transition technology from liquid hydrocarbons to some other energy carrier in the future (Coyle 2007). To avoid complete or catastrophic collapse of the obsolete system, it is advantageous to incorporate components of the old system into the new. Ideally, every system should be designed such that transformation is possible when release and reorganization becomes inevitable. In Table 1, transformability is listed as a trajectory parallel to resilience, and examples are provided in the context of different systems.

When applying resilience thinking into industrial system design, it is important to understand within-scale effects and cross-scale effects of resilience, adaptability, and transformability (Figure 3). At a fixed scale, these 3 concepts are not difficult to understand and differentiate. Resilience and adaptability deal with the dynamics (gradual changes) of the existing system, but transformation is to create a new system by altering fundamental structure and functionality. For example, a biofuel production system may adapt by diversifying final products, such as biodiesel (from corn or other oil) and electricity (for example, for electric cars). The additional energy products could be represented as multiple basins of attraction in the original landscape (Figure 2B-1). However, if the biofuel production system suddenly changes its main products from transportation fuels to other products, such as chemicals, food, plastics, or medicine, the function of the system has likely transformed (Figure 2B-2).

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Figure 3. In-scale and cross-scale effects of resilience, adaptability, and transformability.

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When understanding systems at different spatial and temporal scales or in a nested system consisting of subsystems, the differentiation between adaptability and transformability becomes complicated. First, transformation in subsystems could lead to increased resilience of a larger system. For example, in the past century, when automobiles displaced nonmotorized vehicles, such as the bicycle or horse-drawn carriage, resilience of the vehicle industry at the manufacturing scale was overcome by transformative innovation. However, at a broader scale, the resilience of transportation systems increased because of a sudden increase in vehicle efficiency. In recent years, it has become conceivable that biofuels could be replaced by other energy sources in transportation sector (e.g., electricity), necessitating a change in biofuel production into plastics, pharmaceuticals, or other products. In this case, resilience of the biofuels industry at the manufacturing scale could be overcome by transformative innovation. However, at the broader scale of the transportation system, resilience may be increased because of the use of new fuels that are more efficient, cleaner, and more reliable. Therefore, transformability could occur because of a lack of resilience at subscale but could nonetheless be viewed as adaptation for resilience at a larger scale.

Alternatively, subscale adaptations that increase local resilience could have a cumulative or aggregate effects causing transformation at broader scale. For example, in the short term, a manufacturing system could increase its resilience by renovating and upgrading existing processes and facilities. However, the continuous adaptation for resilience in the short term could eventually lead to a tipping point that causes sudden and dramatic longer-term changes. The example of biofuel industries adding processes for increasing product flexibility could be an example of short-term adaptation paving the way to transformation in the future.

An interesting debate centers on the issue of which of the strategies (adaptation or transformation) is preferable when changes are required to achieve sustainability. Such a decision has to be based on integrated analysis of the systems at multiple scales. For a desirable system with broad social benefits, policy should focus on improving resilience. In contrast, if a system is locked into or trapped in an undesirable state, reducing resilience to allow transformation is a primary concern. Meanwhile, cross-scale effects need to be considered.

As a domestic form of renewable energy, biofuel may be preferable to petroleum-based fuel. However, the current biofuel industry dominated by corn ethanol lacks resilience, especially the ability to adjust to disruptions from markets and supply. Strategies for improving resilience include 1) use of technologies that could process multiple feedstocks and produce multiple products, 2) integration of those technologies to increase flexibility, and 3) minimization of waste or beneficial reuse of byproducts. Nonetheless, as vehicle propulsion technologies develop, transportation systems eventually may not depend on petroleum fuels (e.g., by transitioning to natural gas, hydrogen, or electricity). At that time, biofuel industries will need to transform into other industries. Design of biofuel production systems that plan for system transformation ahead of time could minimize the losses during this process.

EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES

Design for resilience at the process level focuses on proper integration and management of all material and energy flows within and across the production facility boundary. Biofuel technologies can be generally classified as either thermochemical (e.g., gasification, pyrolysis) or biochemical (e.g., fermentation). Several technologies (although not all) are examined from a resilience perspective in Table 2. At the life-cycle scale, additional supply chain stages such as feedstock production, transportation, and distribution of final product should be considered. However, this discussion mainly concerns selecting and managing biomass and its transportation to biorefineries. Dry-mill and wet-mill processes both use fermentation technologies to convert corn grain into ethanol. The dry mill dominates recent investment in new capacity because it is optimized to produce the maximum yield of ethanol per unit corn grain input. After grinding and heating, the grain is sent directly to fermentation for conversion of starches and sugars to ethanol. Fuel-grade ethanol is produced by separating, upgrading, and denaturing the grain liquor (USDOE 2009). The components of the corn grain that cannot be fermented, such as protein, fiber, and fat, can be dried and sold (depending on local markets) as distiller's grain feed for cows or swine. The dry-mill process results in high conversion efficiency of carbohydrate to ethanol but low eco-efficiency because of high life-cycle CO2 emissions and low quality of feed byproduct. By contrast, wet-mill processes separate corn grain components such as the oil and germ prior to fermentation. The separated components are suitable for sale as human food (e.g., corn oil), while only residual sugars and starches are fermented to ethanol (USDOE 2010). The ethanol yield per grain input is lower in wet-mill than in dry-mill processes. Also, capital costs are greater for wet mill than for dry mill (Liska et al. 2009). However, wet-mill plants produce a wider variety of higher quality products, thus increasing diversity to meet different market needs. This situation represents a trade-off between diversity and efficiency in process design.

Table 2. Resilience of different biofuel production technologies
  Characteristics of ResilienceTransformability
DiversityEfficiencyCohesionAdaptability
TechnologyDry-mill fermentationCoproduce distiller grainsOptimum ethanol yield; high cost efficiency; lower eco-efficiency due to life cycle CO2 emissionsEthanol has to be combined with gasoline as transportation fuelLimited flexibilityCould transform to polymer industries
 Wet-mill fermentationCoproduce corn syrup, corn oil, starch, animal feed, and sweetenerLower ethanol yield and lower cost efficiency than dry mill; lower eco-efficiency due to life cycle CO2 emissionEthanol has to be combined with gasoline as transportation fuelLimited flexibilityCould transform to polymer industries
 GasificationCoproduce sulfur, electricity, and chemicals; can treat multiple feedstockHigh carbon conversion efficiency; low cost efficiency; high eco-efficiency by less water use, ash reuse, and use of biomass residuesBiofuels can be directly used in engines; ash reuse and utilization of biomass residues increase linkage to other industriesFlexible feedstocks and productsHigh potential to transform to other industries
 PyrolysisMultiple products (charcoal, bio-oil, gases); can treat multiple feedstocksEnergy and cost efficiency higher than gasification; high eco-efficiency by using biomass residuesLow-quality biofuels; utilization of biomass residues could increase linkageFlexible feedstocks and productsHigh potential to transform to other industries
 Hydrothermal liquefactionMultiple products (bio-oil and gases)High efficiency for high water content biomass; high eco-efficiency by using biomass residuesLow-quality biofuels; utilization of biomass residues could increase linkageFlexible feedstocks and productsHigh potential to transform to other industries

Second-generation biofuels produce ethanol from nonfood feedstocks, such as forest residues, through a pretreatment process called enzyme hydrolysis (Lynd 1996; Larson et al. 2010). Cellulose and hemicellulose components are first hydrolyzed into sugars and then fermented into ethanol (Aden et al. 2002; MacLean and Spatari, 2009). Lignin components in the feedstock cannot be converted to fermentable sugars but can be burned to generate electricity, heat, and steam or to generate electricity for export. Using woody feedstocks has the advantage of overcoming objections raised to the use of food crops for fuel production, greatly expanding the potential feedstock volumes, and likely reducing life-cycle greenhouse gas (GHG) emissions.

Thermochemical conversion of biomass into liquid fuels can be based on gasification, pyrolysis, and direct liquefaction. Gasification converts hydrocarbon feedstocks from solid or liquid to a gaseous mixture (synthesis gas, or syngas) mainly containing CO, H2, CO2, CH4, and N2 (Klass 1998). In gasification-based conversion, the raw syngas has to undergo a series of processes to remove unwanted contaminants and impurities. For example, sulfur can be removed in the cleanup process and recovered as a byproduct. The clean syngas, mainly containing CO and H2, can undergo a catalytic reaction (Fischer-Tropsch type synthesis or biocatalysis) to form liquid hydrocarbons, which can be further upgraded to diesel and gasoline (Dry 1999). With different catalytic reactions, syngas can be converted to other chemicals, such as ethanol, methanol, and dimethyl ether (Huber et al. 2006). Also, syngas can be used directly to produce electricity by powering turbines. This flexibility to produce multiple products increases the diversity of the plant. Meanwhile, the potential to change between different products provides adaptability to changing market prices for heat, electricity, liquid fuels, or specialist chemicals. On the supply side, gasification can break down both lignin and cellulose and therefore can process multiple biomass feedstocks, which may reduce the transportation distances and storage volumes at an equivalent-scale plant and allow feedstock switching in case of supply fluctuations. Compared with biochemical processes, gasification may have higher conversion efficiency of feedstock carbon to liquid fuels, especially for biomass with high lignin content. Moreover, use of biomass residues increases eco-efficiency by reducing life-cycle GHG emissions and also improves cohesion by promoting linkages to other industries (e.g., food processing or municipal landfills). Gasification also has the potential to use less water per gallon of fuel produced than biochemical conversion (Aden 2007; Mu et al. 2010), thus increasing the eco-efficiency of the plant. In addition, the biofuels produced by gasification processes, such as gasoline and diesel, can be directly used in internal combustion engines without the modification of engines, storage tanks, or distribution pipelines required by ethanol, thereby further increasing the cohesion of the biofuel production system. Depending upon the operating parameters of the gasifier, ash with high char (i.e., carbon) content could be produced as a soil amendment for facilitating carbon sequestration (Lovelock 2009). Low char ash could be beneficially reused in the cement industry, further increasing eco-efficiency and cohesion. However, gasification and related facilities require a larger capital investment than fermentation does, which reduces cost-efficiency. If a biofuel plant is designed to treat flexible feedstocks, more energy and investment may be required for biomass handling and pretreatment, reducing overall energy efficiency of biofuel production and increasing costs. Moreover, gasification generates problematic wastewaters containing bio-tars or other byproducts and impurities that must be removed during syngas cleanup processes.

Pyrolysis is the decomposition of organic material by heating in the absence of oxygen (Nan et al. 1994). The 3 products usually produced from pyrolysis are gas, pyrolysis oil, and charcoal. The relative proportions of the 3 products can be controlled by changing reaction parameters and the characteristics of the biomass (Brown and Holmgren 2008). Flexible switching among multiple products increases diversity and flexibility of biofuel production. On the feedstock side, pyrolysis can process multiple biomass feedstocks, including biomass wastes and residues, which could increase flexibility, eco-efficiency, and cohesion. Compared with gasification, pyrolysis has higher energy-conversion efficiency. A large fraction of the biomass energy (50 to 90%) can be converted into a liquid bio-oil (Huber et al. 2006). A pyrolysis reactor is relatively simple, which reduces capital requirements. However, the bio-oil from pyrolysis cannot be directly used in the transportation section because of poor volatility, high viscosity, coking, and corrosiveness (Czernik and Bridgwater 2004). Even after upgrading pyrolysis oil to remove excess oxygen, many impurities may persist. The low quality of the final product reduces the applicability of pyrolysis oils, thus reducing cohesion (linkage to transportation). Therefore, the ideal case may be applying pyrolysis as pretreatment for gasification. Bio-oil from pyrolysis has a low water content and high energy density, which could save energy in transportation and gasification. The energy conversion rate under this scenario may be higher than in direct gasification (Huber et al. 2006), and the charcoal resulting from pyrolysis can be used as a soil amendment to sequester carbon (Mullen et al. 2010).

Direct hydrothermal liquefaction converts biomass to an oily liquid by contacting the biomass with water or aqueous solvent at elevated temperatures (300 to 350°C) with sufficient pressure (12 to 20 MPa) and residence times (>30 minutes, EcoGeneration Solutions 2009). The final products are mainly gases and liquids (Midgett 2008), and the portion of gas to liquids can be controlled by the operating conditions and characteristics of biomass. This technology can treat multiple feedstocks and is especially suitable for converting wet biomass into liquid fuels, because the water can be used as a solvent. These features could increase diversity, adaptability, and cohesion of biofuel production. Compared with bio-oil from pyrolysis, bio-oil from liquefaction is water insoluble and has a lower oxygen content and higher energy content (Huber et al. 2006). However, the quality of bio-oil is still too low to be directly used as a transportation fuel (Huber et al. 2006). Also, liquefaction is a brand-new technology. The largest commercial application is capable of producing 250 tons of bio-oil per day (Peterson et al. 2008).

All processes listed in Table 2 have both advantages and disadvantages relative to resilience. In general, thermochemical conversion technologies may be more resilient because of their high carbon conversion efficiency of biomass wastes and residues, high cohesion, and high flexibility. However, it has low cost-efficiency caused by high initial investment, high cost to treat wastes, and high cost to upgrade intermediary products. Of all thermoconversion technologies, gasification results in the highest-quality products, predominantly CO and H2, from which a nearly infinite array of other products may be synthesized (thereby offering high flexibility). Pyrolysis could be used as pretreatment prior to gasification, especially at feedstock production or biomass collection sites, where volume reduction prior to transportation to a centralized biorefinery could be advantageous (Wright and Brown 2008). In contrast to thermochemical conversion, biochemical conversion technologies are advantaged by high cost-efficiency and less problematic wastes, but biochemical technologies lack cohesion and adaptability.

Feedstock production is an important stage in the life cycle of biofuel production and, with respect to resilience, is heavily influenced by biofuel conversion technologies. Corn grain alone cannot meet projected increases in demand for biofuel. Increasing agriculture inputs for biofuel production would intensify competition with the human food supply or increase potential damage of the landscape by crop cultivation. Therefore, cellulosic biomass will become necessary for large-scale increases in biofuel production. Among all the cellulosic biomass feedstocks discussed, residues or wastes from agriculture (such as corn stover, food processing wastes), the forest industry (bark or wood chips), or cities (municipal solid wastes) are preferred to “energy crops” (such as switchgrass). Use of biomass residues could reduce feedstock costs, increase revenues for farmers or other industries, and increase social acceptance. In addition, using residues could avoid damaging of local landscape and reduce indirect GHG emissions from land change (Fargione et al. 2008). Therefore, from a resilience perspective biomass conversion technologies compatible with residues are more favorable for biofuel production. However, several challenges to using residues remain. For example, removal of stover could upset the nutrient balance of agricultural soils, thus reducing productivity (Food & Water Watch et al. 2007). Use of some wastes could increase contaminants and the environmental burden of biomass conversion processes (USEIA 2003). In any case, it is highly unlikely that one technology will prove to be optimal for all locations, given the geographic diversity of feedstock availability.

Based on the discussions above, some principles are proposed for the design of resilient biofuel production systems:

  • When comparing the efficiency of different technologies, both cost-efficiency and eco-efficiency should be considered.

  • Selecting technologies with multiple feedstocks and products could improve diversity and cohesion.

  • Selecting technologies that can treat biomass residues and wastes could improve eco-efficiency and cost-efficiency.

  • Selecting technologies with flexible products or designing the biofuel refinery with multiple products portfolios could improve the adaptability of a biofuel refinery.

  • Taking advantage of opportunities to sell wastes to other industries or use wastes from other industries could enhance resilience. Beneficial waste reuse and recycling does not only improve eco-efficiency of systems, but also increases linkages of target systems to other systems.

  • Decentralizing feedstock collection sites, combined with the densification of energy content technology (i.e., pyrolysis), could improve cost-efficiency by reducing cost in transportation and take advantage of economies of scale for capital-intensive technologies such as gasification.

  • Use of multiple technologies in combination should be encouraged in biofuel refineries. For example, some refineries could have parallel-operated themo- and biochemical conversions. Some refineries choose to combine technologies in series (i.e., Coskata Inc. 2010).

  • In the short term, corn ethanol production will remain a popular biomass conversion technology because of the sunk costs of existing capital investments and the yield advantage of dry-mill facilities. However, in the near future, technologies that are compatible with cellulosic biomass and residue feedstocks are preferred.

FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES

Based on the foregoing analysis and guidelines for biomass conversion technologies, a novel, flexible-feedstock, carbon-to-liquid (FCTL) fuel process (Figure 4) is proposed to show how resilience thinking could be applied in the design of a biorefinery. It differs from current industry practice in several important ways. First, the FCTL process avoids the controversy associated with use of land dedicated to production of energy crops by focusing on use of biomass wastes and residuals (municipal solid waste, forestry and agricultural residues, waste paper and/or plastics, food processing wastes, and sewage sludge) as the primary feedstock. Based on the “Billion Ton” biomass study by the US Department of Energy (USDOE 2005), residues from forest land and agriculture land could reach 744 million tons in 2050 when large biofuel refineries are likely to exist—enough to provide biofuel to meet more than 1/6 of current transportation fuel demand. In theory, an FCTL process can use different feedstocks in combination based on the availability, price, transportation costs, and associated environmental impacts. Second, through proper process integration, the FCTL process can offer transportation fuels (diesel, gasoline) with coproducts, such as chemicals, electricity, and hydrogen, and can switch among those products. In addition, a wastewater treatment system installed in biofuel production plant could recycle and reuse municipal wastewater and in-plant wastewater as cooling water, thereby reducing freshwater needs. Finally, a point-of-collection pyrolysis process could reduce transportation volumes and costs in processes upstream of an FCTL plant.

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Figure 4. Flow diagram of flexible carbon-to-liquid fuel (FCTL) process.

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A brief analysis of the resilience of an FCTL plant is conducted with indicators developed in this paper (Table 3). Flexible feedstocks and products can improve the diversity and adaptability of the system to changes in supply and markets, which could make the FCTL process deployable over a wider variety of geographic areas. Use of biomass wastes and installation of wastewater treatment can improve eco-efficiency by reducing indirect GHG emissions and freshwater demand. These technologies can improve the cohesion of the system by enhancing the linkages to other industries. Distributed pretreatment design can improve the geographic diversity of biofuel production and improve the cost-efficiency.

Table 3. Resilience of the FCTL process
 Characteristics of ResilienceTransformability
DiversityEfficiencyCohesionAdaptability
Process scaleThe plant can coproduce sulfur, electricity, and chemicalsGasification process increases carbon conversion efficiency; in-plant wastewater reuse increases eco-efficiency; high investment and high energy use reduce cost-efficiencyUse of biomass residue increase linkage to other industries; carbon neutralityPlant can change feedstocks based on their availability and cost; plant can change products flexiblyHigh potential to transform to other industries
Life-cycle scaleDistributed sites of pretreatment increase geographic diversityUsing municipal wastewater increases eco-efficiency; beneficial reuse of ash increases eco-efficiencyUsing municipal wastewater and ash reuse increase cohesion to other industriesDistributed sites of pretreatment increase adaptability 

Nonetheless, there are uncertainties with FCTL that could erode resilience. First, none of the FCTL technologies currently exist at commercial scale, and individual processes are still under development. Second, the technologies used in the FCTL process require high levels of capital investment and consume some of the feedstock energy in operating the system (Jin et al. 2009), which could reduce the cost-efficiency of biofuel production. Waste products generated (such as tar and wastewater) increase treatment costs and environmental liability, which reduces eco-efficiency. Therefore, a quantitative analysis is necessary in the future to match the product mix with feedstock availability within price, emissions, and technological constraints.

Acknowledgements

  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES

This work was funded in part by Purdue University, including the Discovery Park Energy Center, the School of Civil Engineering, the School of Mechanical Engineering, and the Ecological Sciences and Engineering Interdisciplinary Graduate Program. Additional support was provided by the Golisano Institute for Sustainability at Rochester Institute of Technology and by the School of Sustainable Engineering and the Built Environment at Arizona State University. The authors wish to thank Brian C. Pijanowski of the Purdue University Department of Forestry and Natural Resources for his helpful conversations and constructive comments. Nannan Kou also contributed to our understanding of the thermochemical conversion processes discussed herein.

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  1. Top of page
  2. Abstract
  3. INTRODUCTION
  4. ECOLOGICAL RESILIENCE AND SYSTEM DYNAMICS
  5. RESILIENCE THINKING IN INDUSTRIAL SYSTEMS AND BIOFUEL PRODUCTION SYSTEMS
  6. EXAMINATION OF BIOFUEL PRODUCTION TECHNOLOGIES UNDER DEVELOPMENT
  7. FLEXIBLE-FEEDSTOCK, CARBON-TO-LIQUID FUEL PROCESS
  8. Acknowledgements
  9. REFERENCES
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