Advanced Biofuels and Bioproducts || Life Cycle Assessment of Algae-to-Energy Systems

759J.W. Lee (ed.), Advanced Biofuels and Bioproducts, DOI 10.1007/978-1-4614-3348-4_32, © Springer Science+Business Media New York 2013

Abstract Algae-derived bioenergy is being widely discussed as a promising alternative to bioenergy produced from terrestrial crops. Several life cycle assessment (LCA) studies have been published recently in an effort to anticipate the environmental impacts of large-scale algae-to-energy systems. LCA is a useful tool for understand-ing the environmental implications of technology, but it is very sensitive to model-ing assumptions and techniques. In this chapter, the methodological issues surrounding LCA of algae-to-energy systems are reviewed in the context of several of the recent papers with a particular focus on system boundaries, cultivation tech-niques, metrics, coproduct allocation, and uncertainty. The issues raised here are useful in two regards: (1) they enable an understanding of the differences between the published studies and allow LCA practitioners and others to more directly inter-pret the results and (2) they serve as a good starting point for future analysis of algae-to-energy technologies.

1 Introduction

The promise of using algae as a bountiful and renewable source of bioenergy has been attracting increasing attention over the last few decades [ 26 ] . This is because algae have a number of characteristics that make them appealing relative to other bioenergy sources. They are generally fast growing and produce more biomass per area of land than most terrestrial crops [ 19 ] . Certain species generate high concen-trations of lipids so they can be used to produce liquid fuels, such as biodiesel, using existing conversion technologies [ 18 ] . And since they are grown in water, they could

A. Clarens (*) • L. Colosi Civil and Environmental Engineering , University of Virginia , Charlottesville , VA 22904 , USA e-mail: [email protected]

Chapter 32 Life Cycle Assessment of Algae-to-Energy Systems

Andres Clarens and Lisa Colosi

760 A. Clarens and L. Colosi

also be cultivated in man-made ponds, which suggests their cultivation can be scaled up and operated in steady-state mode, greatly enhancing their potential for large-scale energy production. Over the past few years, interest in algae-to-energy tech-nologies has surged for a variety of reasons. Among them is the idea that algae could be used to sequester CO

2 from fossil fuel burning sources, thereby reducing a

major contributor to climate change [ 3 ] . Increasing petroleum prices, concerns about our dwindling fossil fuel reserves, and the perceived competition between food and fuel uses for crops that can be consumed as food have also contributed to interest in algae as a fuel source [ 23 ] .

The heightened attention on algae-to-energy systems has resulted in a prolifera-tion of academic and industrial publications describing these technologies. A num-ber of these studies focus on quantifying the environmental impacts of algae-to-energy systems using life cycle assessment (LCA) techniques [ 8, 14, 24, 31 ] . LCA is a framework for assessing the environmental and energy implications of a process or product over its entire life cycle (LC), from resource extraction to fi nal disposal. Over the past 10 years, LCA has emerged as a valuable tool for understanding the full environmental costs of complex engineering systems. It allows designers and engineers to avoid media shifting, whereby one environmental impact is avoided at the cost of some other, often hidden and worse, environmental burden [ 13 ] . LCA can also serve as a useful design tool that allows for a priori evaluation of different engineering decisions. By applying LCA in this way, it is possible that many tradi-tional sources of pollution can be avoided upstream rather than remediated after they are generated. Even though LCA has been widely practiced for over a decade, only recently have the techniques been applied to algae-to-energy processes.

The algae-related LCA studies appearing in the academic literature to date offer multiple perspectives on how large-scale algae-to-energy systems might be deployed. These studies are largely speculative because there is a lack of empirical data for long-term operation of full-scale commercial algae cultivation systems. In general, the results of algae LCA studies published to date are dif fi cult to compare because of key modeling differences. The differences originate from several stages of the analyses. To begin with, the scope , e.g., system boundaries and functional unit of the studies, is different. Second, the data sources used in the studies, the way in which the studies report their results (i.e., metrics ), and the manner in which they allocate burdens to different processes (e.g., coproducts ) also vary quite a bit. This variability is to be expected given that there are, as yet, no norms for the industry that would suggest the most reasonable set of assumptions. Finally, the unsatisfac-tory way in which the studies handle uncertainty speaks to the lack of data in this fi eld. Table 1 highlights the array of different modeling assumptions that have been used in some of the LCA studies of algae-to-energy systems that have been pub-lished to date. It should be pointed out that each of these studies utilized a different functional unit and many use different modeling assumptions. Thus, it is no surprise that the results are dif fi cult to compare.

In general, it cannot be said that one particular study is more or less “correct” than any of the others. LCA challenges exist even for processes and products that

76132 Life Cycle Assessment of Algae-to-Energy Systems

are well characterized and widely practiced. One widely cited, and related, example is the case of petroleum-based liquid fuels. Since the early 1990s, a substantial number of studies have been conducted describing the process of extracting the crude oil from the ground, transporting it, re fi ning it, distributing and selling it, then burning it in cars and trucks [ 25 ] . Different studies resulted in very different esti-mates for the burdens of similar processes that are practiced in more or less the same manner around the world. To address these challenges, Argonne National Laboratory in the United States created the Greenhouse Gases and Regulated Emissions, and Energy Use in Transportation (GREET) model for estimating LC burdens associ-ated with petroleum-based transportation fuels in 1996 [ 33 ] . By synthesizing the results from various published LC models, and normalizing the system boundaries and allocation assumptions among analyzed cases, the creators of GREET produced a meta-model that is more representative of petroleum fuel production than any one given analysis. This occurs because the meta-model effectively neutralizes (i.e., washes out) some assumptions that can make any one particular study either over- or underestimate the true impacts of a given process. Since the algae-to-energy industry is currently undergoing such rapid development, it seems timely to con-sider standardization of LC methodology to improve the accuracy of LCA for algae-derived fuels.

This chapter is written for two primary audiences. The fi rst is the algae-to-energy researchers wishing to model LC impacts of speci fi c products or processes. For these uses, the material presented here should serve as a useful primer into the lan-guage of LCA as it relates to algae-to-energy processes. The second audience is the broader scienti fi c and journalistic community. This community has occasionally misinterpreted the results of several recent algae LCAs. The material presented here should help educate the science-literate reader who has no background in LCA so that they can better understand the implications and conclusions of algae LCA studies. It is expected that successful engagement of both audiences should improve the quality of future algae LCA studies and contribute to discourse about the merits of algae-to-energy technologies.

Table 1 Select LC modeling assumptions for several studies appearing in the academic literature to date

Study FU Data sources Coproducts Uncertainty?

Stephenson et al. [ 31 ] 1 ton biodiesel NREL US LCI Digestion/electricity No Campbell et al. [ 5 ] 1-km diesel truck Australian LCI Digestion/electricity No Jorquera et al. [ 14 ] 1 ton dry solids Literature review None No Clarens et al. [ 8 ] 317 GJ EcoInvent None Yes Lardon et al. [ 17 ] 1 MJ fuel EcoInvent Glycerol No

FU functional unit; NETL US LCI National Renewable Energy Laboratory of the United States Department of Energy Life Cycle Inventory Database [ 20 ] ; Australian LCI Australian National Life Cycle Inventory Database; EconInvent Swiss National Life Cycle Inventory Database [ 34 ]


2 Goal and Scope De fi nition

A life cycle analyst’s motives for carrying out an LCA can have important implications for the results of a study. These “zero order” assumptions are often rooted in the type of LCA being performed. LCAs fall broadly into one of two categories. Attributional LCAs are those in which all of the environmental impacts associated with a product or process are compiled and reported [ 1 ] . Consequential LCAs are those that evaluate the impacts of making a particular change to a process or prod-uct, or compare two technologies with related functions. Consequential LCAs are often more straightforward to perform because they permit for the canceling of unit processes or systems that are common between the technologies of interest. In the case of algae, most published LCA studies are attributional since there are few tech-nological systems existing to which algae-to-energy can be compared. There is, however, an important role for consequential LCA as this fi eld moves forward; since they can help identify and quantify what impacts might arise from evolving algae technologies. The decision to undertake an attributional or a consequential LCA is manifest most notably in decisions about the system boundaries and func-tional units of the study. System boundary decisions include all the elements associ-ated with geographic areas, natural environments, time horizons, and others. The functional unit is the quantitative basis for the life cycle comparison and differs depending on the processes to be compared. Both are explored here.

2.1 System Boundaries

Most of the research on algae-to-energy systems carried out to date has been at the bench or demonstration scale [ 18 ] . This makes it dif fi cult to say with much certainty what a full-scale algae-to-energy industrial facility would look like and herein lies one of the fundamental challenges of developing reliable LC estimates for algae production. Using best engineering judgment, it is possible to design hypothetical algae-to-energy facilities, but naturally, there is variability among these designs (Fig. 1 ). For example, one modeler might assume that algae should be cultivated in ponds, while another could assume photobioreactors [ 6 ] . Similarly, a belt fi lter press could be modeled as means to separate algae from the growth medium, whereas self-cleaning bowl centrifuges might be a viable alternative [ 24 ] . Both unit opera-tions carry out the same dewatering function but with different requirements in terms of inlet and outlet concentration, demand for chemical fl occulants used to accelerate the settling of the algae out of solution, and energy use pro fi les. Similarly, there are several technically viable options for extraction of oil from algae biomass, namely: sonication [ 28 ] , bead mills [ 7 ] , and enzymatic processes [ 11 ] . For conver-sion of algae biomass into biodiesel, one might choose decarboxylation of fatty acids [ 29 ] and digestion of non-fatty acid fraction [ 27 ] or the conventional transesteri fi cation route. Finally, the end-product of the algae-to-energy facility can


also vary, because biodiesel is not the only energy carrier that can be produced from alga biomass [ 3 ] . It can be dried and combusted directly to generate electricity or it can be separated such that the carbohydrate fraction may be fermented to produce ethanol [ 24 ] . Naturally these two systems would have very different impacts.

As an example of the way in which systems boundaries selection can impact LCA results and conclusions, it’s informative to consider two of the more thoroughly documented algae LCA studies that have been published to date: Clarens et al. [ 8 ] and Stephenson et al. [ 31 ] . Clarens et al. [ 8 ] used an energy-basis functional unit and only modeled cultivation-phase burdens for open pond systems. They did not account for the possibility that energy production from algae might also create valuable coproducts since they argue that it is still unclear whether there will be tenable mar-kets for these coproducts. In contrast, Stephenson et al. [ 31 ] utilized a functional unit of 1 ton algae biodiesel to compare between open pond cultivation systems and pho-tobioreactor cultivation systems. These authors included two types of valuable coproducts: electricity, as produced via combustion of natural gas generated during anaerobic digestion of residual (non-lipid) algae biomass, and glycerin. In light of these dramatically different sets of systems inputs, it’s not surprising that each study reached different types of conclusions. Clarens et al. found algae-derived biomass energy to be generally more environmentally burdensome than corn, canola, or switchgrass alternatives. In contrast, Stephenson et al. found algae-derived biodiesel to be more environmentally bene fi cial than fossil-derived diesel.

Once an algae-to-energy process has been speci fi ed there is the additional uncer-tainty associated with setting system boundaries. LCA is typically intended to cap-ture all of the environmental impacts of an engineered system. Naturally, in a highly interconnected technical world, system expansion results in models that become impossibly large and complex. For example, to produce carbon dioxide for use in industrial processes, it is necessary to model ammonia production since most of the carbon dioxide in this country comes from the steam reforming of hydrocarbons to produce hydrogen, most of which is used to produce ammonia via the Haber–Bosch process [ 21 ] . This in turn requires that we understand something about the way

Fig. 1 In selected system boundaries for an algae LCA study, one must typically select from ( a ) or capture all of ( b ) a large number of possible unit operations


natural gas is produced and transported in this country and the countless unit operations that allow us to purchase a canister of relatively pure carbon dioxide for the factory. To cope with this complexity, many LCA practitioners have set arbitrary boundaries around their processes of interest. For example, one study might state that any process contributing less that 5% of the total mass or energy or other impact to the fi nal total is neglected. In this way the problem can be distilled down to some-thing that is not computationally expensive and still yields good approximations of a process’ impact.

Beyond system design and boundary setting, LCA analysts may chose to focus on speci fi c pieces of a larger system to provide a desired level of resolution. For example, in their work, Clarens et al. considered only the cultivation of algae argu-ing that the uncertainties with that fi rst step in the algae-to-energy life cycle should be addressed [ 8 ] . By focusing only on cultivation, the authors were able to explore the full implications of that important LC stage including crucial upstream impacts such as fertilizer production and carbon dioxide generation and delivery. In fact, a sensitivity analysis included in this chapter suggests that these two impacts are among the most important factors driving the overall life cycle burdens of algae production. Many of the other studies assume that the upstream impacts of deliver-ing fertilizers and carbon dioxide should not be included. In Sander and Murthy, a cut off of 5% was assigned to LC contributions that would be neglected in the analy-sis [ 24 ] (Fig. 2 ). This represented the most rigorous treatment of boundaries from any of the studies published to date. However, this study also made certain assump-tions, notably, that the ef fl uent from a secondary wastewater treatment plant would contain enough nutrients to sustain a community of algae [ 4 ] . This assumption is not supported by stoichiometry or by the bench-scale research and as a result their estimates for algae life cycle impacts are most likely low.

2.2 Functional Unit

In all LC studies, a reference fl ow is needed to which all other modeling fl ows of the system will be related [ 13 ] . This fl ow must be a quantitative measure and for some industries, e.g., steel, the choice is usually obvious like X kg steel at the foundry. In other cases, including algae-to-energy systems, this decision can be more complicated. Recent studies have selected a wide variety of functional units (FU) including volume of biodiesel, dry mass of algae produced, kilometers of truck transport, and total energy embedded in the algae assuming the biomass is burned (see Table 2 ). All of these FUs are valid bases from which to evaluate algae LC, but this diversity in FUs does not make for straightforward comparison between studies. The lack of consensus on a standard FU re fl ects the lack of indus-try agreement on what the best products to make with the algae will be. Some of the assumptions about goal and scope setting carry over into the functional unit since a FU of liters of biodiesel will inherently exclude the value that could come from a by-product such as ethanol.


Fig. 2 Many studies assume that the upstream impacts of delivering fertilizers and carbon dioxide should not be included. A cut off of 5% was assigned to LC contributions that would be neglected in the analysis (from Sander and Murthy [ 24 ] )


In LCA more broadly, FUs sometimes require that a performance constraint be applied in order to normalize between dissimilar systems. A carpet, for example, is quieter than a wood fl oor, even if the latter is more durable. Using a square meter of fl ooring as the functional unit may overlook performance characteristics (noise buffering and durability) that will ultimately impact the analysis [ 1 ] . In the case of algae, performance constraints are certainly limiting in a few important ways. When benchmarking algae to other terrestrial crops, it is useful to apply an FU that is com-monly accepted by the biofuels industry. Though bushels of corn or liters of ethanol do not apply directly, analogs are possible. For example, algae might be compared in terms of dried biomass generated per unit area or liters of biodiesel produced per unit area per time. Energy content can be used as an FU, though it can overlook important differences between biomass. Algae may have a high heating value com-parable to switchgrass though in practice, converting algae to usable fuel is quite a bit more straightforward.

3 Metrics

After the goal and scope of a study have been speci fi ed, a life cycle inventory (LCI) is typically carried out. The LCI is the accounting stage in which all the physical fl ows are reconciled with known emissions data to quantify the environmental bur-dens and resource requirements over the entire life cycle [ 1 ] . The outcome from this process is typically an exhaustive list of emissions factors; many more than can be reasonably expected or necessary in a report. Therefore, an important step in devel-oping an LCA is the process of simplifying raw LCI data into speci fi c metrics. Table 2 lists the impact metrics used in a few recent LCA papers of algae-to-energy systems. The differences in study endpoints contribute to the dif fi culties in compar-ing the results. The decision to include some metrics and exclude others can have important implications for the results and interpretation of the study. Most LCA guidebooks divide impact categories into three principle categories: resource use, ecological consequences, and human health [ 1 ] . Each category is discussed brie fl y here in the context of algae-to-energy systems.

Table 2 Impact factors or metrics selected in several algae LCA studies

Study Impacts

Stephenson et al. [ 31 ] GWP, energy use, water use Campbell et al. [ 5 ] GWP, energy use, land use Jorquera et al. [ 14 ] Energy use Clarens et al. [ 8 ] GWP, land use, eutrophication, water use, energy use Lardon et al. [ 17 ] Abiotic depletion, acidi fi cation, eutrophication, GWP , ODP,

human toxicity, marine toxicity, land use , ionizing radiation, and photochemical oxidation

Italicized metrics are common to multiple studies GWP global warming potential; ODP ozone depleting potential


3.1 Resource Use

Resource use is the most straightforward of the impact factor categories because the metrics involved are typically simple sums of fl ows from the environment. For example, total nonrenewable energy use, normalized by energy content, is a com-monly encountered metric. Total land use is an important resource metric that has been hotly debated by the life cycle community because of the important upstream or indirect land use that is required to maintain the productivity of the agricultural region (e.g., land associated with production of fertilizer) or because of land could be used for alternative uses if not for agriculture (e.g., primary growth forest). Similarly, total water use is a resource that is relevant for most biofuel life cycle studies as shown in recent work [ 9 ] . An important distinction when it comes to water use is that of consumptive vs. nonconsumptive use. Most energy generation facilities use a large amount of water, primarily for cooling, so even though the amount of water needed for these systems is large, a comparatively small amount of the water is actually consumed [ 16 ] .

Most models of biofuels systems include, at a minimum, total net energy use as a metric. This is an obvious and important metric because many biofuels such as ethanol consume a considerable amount of fossil fuels to generate a certain amount of ethanol. Recognizing that biofuels are not worth pursuing if there is no energetic gain, many studies have explored the net energy balance associated with alternative energy options. Algae-derived energy is no exception, and several studies report on the energy that is required to produce energy carriers from algae. Whether these estimates are net positive or net negative depends on the modeling assumptions selected in the study. In addition to energy use, there are at least two other impact factors that should be considered when evaluating algae-to-energy systems. The fi rst is land use. Algae grow more ef fi ciently than terrestrial crops, and so quantify-ing this parameter is important as a means to highlight one of algae’s most pro-nounced advantages. Similarly, water use is an important parameter since large-scale algae cultivation is likely to require large volumes of water. How much, and how this relates to the water use of terrestrial crops is likely to be an important factor in water-limited growing regions. Including water as a key metric is important.

3.2 Ecological Consequences

A number of common metrics to describe ecological consequences are included in most LCAs. The most common example is global warming potential (GWP) which normalizes greenhouse gas emissions into one number with units of mass emissions in carbon dioxide equivalents. Since several chemicals typically contribute to speci fi c ecological impacts, metrics are very useful for consolidating data. Other examples of common metrics in this class are ozone depleting potential, eutrophica-tion potential, and acidi fi cation potential.


The most obvious ecological consequence to include in algae-to-energy studies is GWP since many algae-based energy systems are designed to produce intrinsi-cally low carbon neutral fuels. Because of this desire to produce low carbon fuels, many algae projects have used “sequestration” to describe their activities. In reality, algae-to-energy systems are not a sequestration technology. Sequestration implies that there is long-term storage of CO

2 either as a solid carbonate mineral or in the

subsurface under high pressure. In theory, algae could be grown and the biomass buried to sequester carbon, but it would be necessary to carefully control the condi-tions under which the carbon was buried such that the biomass was not simply digested by bacteria that could generate methane, effectively compounding the problem. What algae-to-energy systems can offer is a fuel that is closer to carbon neutral than conventional fossil fuels. That is, most of the carbon that will be emit-ted from the combustion of the fuel is not new carbon removed from the ground as in the case of coal or petroleum. This won’t help mitigate the impacts of climate change by reducing atmospheric concentrations, but it will reduce the increase in this concentration by not contributing new carbon. How much carbon these pro-cesses can keep out of the atmosphere is a current topic of investigation. It is impor-tant for the industry to adapt norms with regard to the way it treats carbon dioxide for full transparency.

3.3 Human Health

Human health metrics are often overlooked in the analysis of alternative energy sources. Part of the reason for this is that, of the three classes of inputs surveyed here, these tend to have the highest embedded uncertainty. The exposure to hazard-ous substances varies signi fi cantly, and this can greatly impact the results of an analysis. Further, since limited toxicological data is available for many compounds, developing reliable causal relationships is a challenge. When impacts can be quanti fi ed in a life cycle context, they are often reported in terms of disability-adjusted life years.

Ignoring the contribution that human-health indicators may have on algae-to-energy life cycle studies could be an important oversight for several reasons. The most dangerous substances on the United States Environmental Protection Agency list of carcinogenic chemicals reveals that many are agricultural chemicals. If algae are deployed as an alternative to terrestrial agriculture, which is heavily reliant on harmful herbicides, fungicides, and pesticides, there could be a net advantage to adopting aquatic species for biomass generation. Of course, the algae cultivation sector is too young to know whether it will require signi fi cant fl ows of agricultural chemicals to cope with pests or other problems. Similarly, the water quality implica-tions of large-scale algae cultivation could have mixed impacts. On the one hand, algae could remove contaminants from water sources, serving effectively like a large ecosystem-level “liver” for toxin removal. On the other hand, algae could excrete low levels of toxic chemicals as exempli fi ed by coastal red tides. In short,


any large-scale production of algae is likely to have some human health conse-quence and even though it is dif fi cult to predict how those will manifest at this early stage, it is not dif fi cult to anticipate that better tools will be needed to understand these relationships as the technology matures and becomes deployed.

3.4 Metrics for Assessing Algae LC Impacts

Based on this discussion, there are at least four metrics that should be included in life cycle studies of algae-to-energy technologies:

Net energy • GWP • Land use • Water use •

Net energy is important because efforts to use algae for fuel production are predi-cated on the assumption of a positive net energy balance. Similarly, GWP is impor-tant because of the expectation that algae-to-energy systems will be no more carbon intensive than conventional fossil fuels. In addition, land use and water use should be considered because of algae’s high productivity relative to terrestrial crops and its unique requirements for water that set it apart from other sources of bioenergy.

4 Data Sources

A perennial problem with any LCA is identifying reliable and representative data sources. LCI data are available for many common raw materials (e.g., polyvinyl chloride) and manufacturing processes (e.g., extrusion), and, generally speaking, the more common a process, the better characterized it is from an LC perspective. Naturally, having multiple sources of data for a single process allows the user to evaluate the reliability of each source. In the production of algae, there are a large number of materials and processes that have been modeled from a life cycle stand-point that are quite useful. For example, reliable inventory data for a number of fertilizers, fl occulants, and other industrial chemicals is readily available from a number of sources as highlighted in Table 3 . Similarly, unit operations like pumping centrifugation can be easily modeled from fi rst principles to derive energy use under conditions relevant to the speci fi c process of interest [ 22 ] .

As discussed earlier, current studies are somewhat limited by the fact that few full-scale algae-to-energy facilities are in operation. This makes it dif fi cult to esti-mate the emissions from speci fi c applications. For example, fugitive emissions from open ponds are expected to be nontrivial, and loss of this nutrient-rich medium could impact nearby receiving waters. Estimating this potential for eutrophication is highly speculative until actual ponds are in place from which data can be collected.


Similarly, there is little data to support assumptions about how often tubular photobioreactors would crack and require replacement, or the extent to which geotextiles are needed at the bottom of an open pond to prevent seepage of growth medium into the subsurface. Most of these estimates will be generally unreliable until some pilot plants are built in the coming years. In the meantime, analogous processes can sometimes be used to approximate the emissions associated with algae-related unit operations. For example, belt fi lter presses in wastewater treatment sludge handling can be used to approximate the impacts from an algae-to-energy unit operation, and as such, have been used by a number of authors [ 8, 31 ] .

5 Allocation

Allocation refers to the broad category of assumptions that are needed to disaggre-gate highly interconnected industrial systems such that environmental impact can be assigned to speci fi c processes. Since these decisions often introduce subjectivity into the analysis, the ISO (International Organization for Standards) standard for LCA effectively recommend that whenever possible, allocation decisions should be avoided [ 13 ] . Allocation questions arise often in LCA for processes that are multi-input (e.g., land fi lls), multi-output (e.g., oil re fi neries), or in which recycling occurs between processes (e.g., using coal fl y ash from coal power production as a cement substitute) (Fig. 3 ). To study the LC of asphalt production, for example, it is possi-ble to collect re fi nery-wide emissions estimates, but a question will remain about how to assign these impacts to asphalt as opposed to the other outputs from the plant such as gasoline, diesel, lubricants, and so on. The emissions can be allocated based on estimates of relative mass fl ow rates or the relative economic value of the out-puts. Frequently, neither of these seems particularly satisfactory, because neither allocation rule has particular physical signi fi cance. What ISO recommends instead is to increase the level of detail of the model to tease out physical relationships between processes or products and speci fi c environmental burdens. In the re fi nery

Table 3 Key data common to most algae-to-energy LC models and sources of data

Purpose Data sources

Unit operation Pumping (gas, liquid) Move water and gases Weidema [ 34 ] ; Perry and Green

[ 22 ] ; Stephenson et al. [ 31 ] Mixing (of medium) Maintain suspension Dewatering Separate algae and medium Homogenization Cell lyses Separations (of oil) Separate oil from biomass Transportation Move products

Material/Energy Electricity Pumping, other unit ops. NREL [ 20 ] ; Weidema [ 34 ] Natural Gas Drying Fertilizer (N and P) Cultivation Flocculent Separations


example, this would involve zooming in on the workings of the re fi nery to identify which speci fi c unit operations are required for asphalt production and then only include those. The ISO standard acknowledges that allocation decisions are a major source of subjectivity in most LCAs.

In the life cycle modeling of algae-to-energy systems, there are several multi-input or multi-output processes that are likely to in fl uence the environmental burden calculations. For example, when modeling the life cycle burdens of using an algae-derived fuel, it is likely that the fuel will be burned as a mixture with petroleum-based fuels. To account for the burdens assigned speci fi cally to the algae content of the truck’s fuel will require allocation. Similarly, coproducts from algae cultivation have been widely discussed since there are signi fi cant life cycle (and economic) credits to be had for producing high-value by-products along with an algae-derived energy source. At present, there is no consensus on how to allocate the burdens of coproducts in algae production, in large part because the chemistry of these by-products ranges greatly. A proposed algae-to-energy facility might ferment a por-tion of the non-lipid fraction of the cells to produce ethanol and assign itself credit for this production. But should this facility receive credits for avoiding the produc-tion of corn ethanol at some other location? Another plant might produce high-value pharmaceutical additives. Until normative assumptions are developed in the fi eld it is imperative that researchers are transparent about their assumptions.

The delivery of large volumes of CO 2 , a waste product from many industries, to

algae cultivation facilities requires some allocation judgments, which can impact the results. Most industrial carbon dioxide in developed countries is a by-product of ammonia production. An ammonia plant can be modeled and the emissions quanti fi ed, but how much of the burden should be assigned to carbon dioxide vs. ammonia? An idealized plan produces about the same amount of both, but ammonia is the higher economic value product. Carbon dioxide is captured as a valuable by-product but without the ammonia, the plant would not exist. Some in the LC fi eld argue that carbon dioxide should have burdens allocated to it using market price of the two commodities, even though this is an imperfect metric since prices change over time. Others suggest that the burdens should be allocated using a mass balance, but again here, this strategy neglects the fact that the facility exists to produce the more high-value product, ammonia.

Fig. 3 Allocation decisions in algae-related LCA analysis can be broadly categorized into processes where ( a ) there are multiple inputs, ( b ) there are multiple outputs, or ( c ) there is recycling occurring between multiple sectors. In all cases decisions are required about how to divide life cycle burdens and these can have large effects on the fi nal results


6 Uncertainty

There are at least three types of uncertainty associated with most LC studies. The fi rst has to do with modeling inputs. Although many LCA practitioners utilize a single average value for modeling inputs, all parameters generally exhibit a range of values in the real world and all measurements are subject to some unknown error. Key examples of algae modeling parameters that may have wide ranges of values or unknown measurement errors include algae yield; algae lipid content; conversion ef fi ciency; and even life cycle impact factors (energy use, GWP, etc.) for material inputs such as electricity from the US grid, etc. The second type of uncertainty is associated with spatial and temporal differences in systems operation. These sys-tematic differences in time and location can have important effects on LCA results; (e.g., it is reasonable to expect higher algae yields in sunnier parts of the country). The third and fi nal type of uncertainty arises from extrapolation of bench-scale data to hypothetical full-scale systems. This type of uncertainty is largely unavoidable at present, in the absence of many full-scale algae-to-energy systems that have been in operation for any appreciable length of time.

Stochastic tools have become increasingly important for bounding uncertainty in LCA over the last few years (Fig. 4 ). Monte Carlo analysis is one of the common stochastic tools used by practitioners [ 30 ] . This method is useful for quantifying a range of probable output values from a series of input variables which have been assigned empirical or theoretical distributions. These distributions make it possible to encapsulate the three types of uncertainty referenced in the previous paragraph. Repeated sampling from the input distributions creates distributions of output val-ues, which can be parameterized to give empirical estimates of mean or median. Empirical uncertainty for output parameters can also be quanti fi ed using standard deviations, standard errors, or percentiles [ 10 ] . Most life cycle software (e.g., SimaPro and GaBi) now include stochastic toolkits to perform Monte Carlo and related analyses. For LC practitioners using spreadsheet-based models, a number of commercial add-ins (e.g., Crystal Ball ® and @Risk ® ) allow for fl exible management of input and output distributions in models. It should be noted that few of the life

Fig. 4 Stochastic tools, such as Monte Carlo analysis, are receiving increasing attention from LC practitioners as means to systematically incorporate uncertainty into their analysis. Here, the pro-cess by which uncertainty in inputs is propagated through a spreadsheet model into empirical estimates of probabilistic output is demonstrated using screen shots from the CrystalBall Monte Carlo tool. ( a ) input distributions, ( b ) model, ( c ) stochastic outputs


cycle studies published to date have included uncertainty, largely because data availability is a limiting factor and the computational complexities are nontrivial. Moving forward, it will be necessary for algae life cycle models to address this uncertainty in a systematic fashion.

7 Interpretation

The fi nal step in performing an LCA is interpretation of the results to highlight principal themes emerging from the study. In the process of conducting an LCA the analyst should develop a deep understand of the relationship between the model structure, assumptions, inputs, and the model outputs. The analyst should highlight the most important relationships for readers who lack the time or expertise to repro-duce the analysis. The analyst is also tasked with developing broad conclusions from the analysis. Clearly, this process lends itself to subjective interpretation of results and must be handled carefully to ensure the results are as transparent and useful as possible.

One of the most common methods for minimizing subjectivity in data interpreta-tion is to perform a sensitivity analysis in which the connection between modeling inputs and outputs is quanti fi ed. For example, Clarens et al., used a sensitivity anal-ysis to report the top fi ve input parameters driving energy use and greenhouse gas emissions during algae cultivation [ 8 ] (Fig. 5 ). The results, shown in Fig. 5 , illus-trate how the model outputs respond to a change of ±10% on the input parameters in turn. From these results it is clear that algae high heating value (i.e., lipid con-tent), fertilizer production and application, and CO

2 production and application are

driving the burdens. An important element of data interpretation is understanding how errors in the

model could propagate and impact fi nal results. Errors can be introduced into the model in several ways, including inaccurate or poorly transcribed data sources, inaccurate relationships in the model, or unrealistic modeling assumptions. A com-mon source of error in LCA models is double counting in which one emission is

Fig. 5 In LCA sensitivity analysis allows for understanding both the sources of variability and for performing sensitivity analysis (adapted from Clarens et al. [ 8 ] )


incorporated into multiple metrics. Some reactive nitrogen species, for example, can contribute to eutrophication of surface waters and global warming.

One of the most effective ways to interpret the results of an LCA and understand whether there are sources of error is to benchmark the results to related studies. In the case of algae, this step has been largely ignored, probably because there was little prior literature until recently. This does not mean that comparing data to analo-gous systems is not worthwhile. To illustrate this, Fig. 6 shows the energy use required to produce biodiesel from algae from four different studies (one paper has two cases). Following adjustment to a standardized functional unit of 1,000-L algae biodiesel (Fig. 6a ), the values are compared to the results from conventional soy biodiesel, a thoroughly characterized process (dotted line) as reported by Hill et al. [ 12 ] . A preliminary comparison of the results (Fig. 6a ) suggests that algae are either much better or much worse than conventional soy biodiesel. Based on this compari-son alone, it would be dif fi cult to say anything de fi nitive about how favorable algae biodiesel may be relative to soy biodiesel.

Figure 6b summarizes the same results following adjustment of functional unit and system boundaries. As expected, these adjustments make the results of the four algae LCA papers more consistent. This increase in uniformity among stud-ies can be quanti fi ed using coef fi cients of variation (CV), where CV is de fi ned as the ratio or standard deviation to mean value. CV in Fig. 6a , re fl ecting only nor-malization of the functional unit, is 1.39. From Fig. 6b , we see that CV is dramati-cally reduced, to 0.46, following manual adjustment for system boundaries. This decrease emphasizes the substantial impact of systems boundaries selection, here standardization of upstream nutrient burdens and coproduct allocations, on the outcome of algae LCA studies. A third and fi nal normalization can be carried out in which key model assumptions regarding algae attributes and separations/drying parameters are made uniform across all studies. These parameters have been identi fi ed by one or more authors as model inputs that are especially critical dur-ing LCA of energy production from algae. Results from this fi nal step of the assimilation analysis are presented in Fig. 6c . This increase in uniformity among selected studies enables more meaningful comparison between algae biodiesel and an external benchmark, as shown visually in the fi gure. In Fig. 6c , the various estimates for algae biodiesel, derived independently, then normalized, are very close to the estimate for soy biodiesel.

During data interpretation, it is common to incorporate other elements that are exogenous to the LC model but which could inform analysis of the results. One common example of this is the incorporation of economic drivers into the model. Campbell et al., for example, performed a combined economic and environmental life cycle analysis of producing biodiesel from algae grown in near-shore salt-water ponds in Australia [ 5 ] . The results of this study suggest that, based on GHG emis-sions alone, algae perform favorably relative to conventional terrestrial crops. This study is noteworthy because it is the only one to consider growing the algae in salt water. Given the tremendous potential to grow salt water species on marginal, near coastal waters, this is an approach that has been experimentally proposed in several papers by Chisti but for which little life cycle modeling results exist [ 6 ] .

Fig. 6 A comparison of several recent LCA analyses of algae-to-energy systems depicts the energy use needed to produce biodiesel from algae cultivated in raceway open ponds following standard-ization of functional unit. ( a) Depicts the raw data following adjustment of functional unit to 1,000 L biodiesel. ( b ) Depicts data from ( a ) following additional adjustment to system boundaries (i.e., upstream nutrient burdens and coproduct allocation). ( c ) Data from ( b ) following additional stan-dardization of key drying assumptions. Dotted line represents energy use associated with produc-tion of one functional unit (1,000 L biodiesel) from soybeans [ 12 ] . CV coef fi cient of variation


8 Conclusions

This chapter surveyed some of the key challenges associated with utilizing LCA methodologies for studying algae-to-energy technology. These challenges have emerged over the last two years as a large number of systems-level life cycle studies of proposed algae-based energy technologies have appeared in the academic literature. Before 2009, only a few algae-to-energy LCAs had been published and even these were only nominally LCAs [ 15 ] . The assumptions about cultivation and drying that were used in these studies were not highly representative of previ-ously published reports. The recent work better re fl ects the way that the industry expects algae-to-energy systems will be deployed in the fi eld, but the results are dif fi cult to compare directly because of the varied boundaries and assumption speci fi ed by the authors. This chapter has highlighted most of the normative judg-ments faced by LC practitioners and discussed each in the context of algae-to-energy systems in order to support future work in this area.

From the existing literature, several themes begin to emerge that will assist in designing future analyses. One of the most common is that recent LCAs echo many of the conclusions of the fi rst-generation algae research conducted in the 1980s and 1990s. These studies suggested many of the system’s-level implications of large-scale algae deployment [ 35 ] . Benneman’s report to the United States Department of Energy concluded that open ponds would be the only economically viable way to grow algae for sequestering CO

2 from power plant fl ue gases [ 2 ] . Similarly, Votolina and others

have suggested that algae-based wastewater treatment would be a technically com-petitive approach for conducting tertiary treatment of wastewater [ 32 ] . In both cases, these conclusions are well aligned with the results of more recent LCA studies, even though these early reports never use the term “LCA.” A second important theme is that algae-to-energy systems have a long way to go technologically before they are viable from an environmental burden standpoint. In this regard, LCA is a powerful design tool because it allows for a focus of R&D on those processes that will have the most signi fi cant impact on reducing the burdens of the processes as a whole.

In light of the large amount of investment in the algae-to-energy fi eld, it is likely that LC tools will continue to be used to understand and assess the impacts of these emerging technologies. In order for these studies to be more immediately compa-rable, it is important that the community develop nominal assumptions about how to handle algae systems. This chapter can serve as a fi rst step toward developing these norms.

Acknowledgments The authors gratefully acknowledge funding for this study from a UVA Fund for Excellence in Science and Technology Grant.


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