Algal Research 4 (2014) 116–122

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Algal Research

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Microalgae to biofuels lifecycle assessment — Multiplepathway evaluation

Jason C. Quinn a,⁎, T. Gordon Smith b, Cara Meghan Downes c, Casey Quinn b

a Utah State University, United Statesb Colorado State University, United Statesc New Mexico State University, United States

Abbreviations: CO2-eq, carbon dioxide equivalent; DWgas; LCA, lifecycle assessment; LEA, lipid extracted algae;supercritical carbon dioxide; T&D, transportation and dIPCC, International Panel on Climate Change.⁎ Corresponding author at: 4130 OldMainHill, Logan, U

435 797 0341.E-mail address: Jason.Quinn@usu.edu (J.C. Quinn).

2211-9264/$ – see front matter © 2013 Elsevier B.V. All rihttp://dx.doi.org/10.1016/j.algal.2013.11.002

a b s t r a c t

a r t i c l e i n f o

Article history:Received 3 April 2013Received in revised form 19 September 2013Accepted 6 November 2013Available online 4 December 2013

Keywords:Lifecycle assessmentMicroalgaeModelGreenhouse gasesNet energy ratioAnaerobic digestion

A variety of researchers have constructed and presented lifecycle assessments of the microalgae-to-biofuel pro-cess, however, inconsistencies in system boundary definitions and high-level process modeling have led to awide range of results. This study integrates engineering process models validated through experimental andmodeling research to perform an environmental assessment of four microalgae-to-biofuel production scenariosleveraging the Argonne National Laboratory GREET model. The baseline scenario consists of a down flow openpond growth system, three phase de-watering step (settling, dissolved air flotation, and a centrifuge), hexane ex-traction and nutrient recovery using anaerobic digestion. The net energy ratio (NER), defined as energy con-sumed over the produced energy, and greenhouse gases (GHG) for the baseline scenario are 0.7 MJ MJ−1 and−41.7 g CO2-eq MJ−1 respectively. Three alternative scenarios are also evaluated: 1) Improved microalgal pro-ductivity, 2) supercritical CO2 extraction, and 3) no nutrient recycle. This research shows that supercritical CO2

extraction is neither currently energetically- nor environmentally favorable and that nutrient recycle plays an in-tegral role in achieving favorable NER and GHGs. The study highlights on the systems level, two findings relatedto the NER; 1) the NER is minimally impacted with increased productivity and 2) increasing microalgae lipidcontent detrimentally affects theNERwhich is attributed to the reduction in the total energy that can be capturedby the anaerobic digester.

© 2013 Elsevier B.V. All rights reserved.

1. Introduction

The next generation of biofuel feedstock processes must be criticallyanalyzed to quantify the potential scalability and corresponding envi-ronmental impact. Compared to first-generation biofuel feedstocks,microalgae are characterized by higher solar energy yield, year-roundcultivation, the use of lower quality or brackish water, and the use ofless- and lower-quality land [1–6]. The theoreticalmaximumproductionof oil from microalgae has been calculated at 350,000 L·ha−1·yr−1

(38,000 gal·acre−1·yr−1) and is dramatically larger than that oftraditional terrestrial crops [7]. Scalable experimental data haveshown a near term realizable production of 46,000 L·ha−1·yr−1

(5000 gal·acre−1·yr−1), compared to 2500 L·ha−1·yr−1 (270 gal·acre−1·yr−1) of ethanol from corn or 580 L·ha−1·yr−1 (60 gal·acre−1·yr−1) of biodiesel from soybeans [8–12]. Researchers haveshown that microalgae feedstock cultivation can be coupled with

, dry weight; GHG, greenhouseNER, net energy ratio; SC-CO2,istribution; VS, volatile solids;

T 84322, United States. Tel.:+1

ghts reserved.

combustion energy plants or other CO2 sources and has the potentialto utilize nutrients fromwastewater treatment plants [3]. These advan-tages have led to a continuing interest in microalgae as an alternativefeedstock for biofuel production.

Lifecycle assessment (LCA) has emerged as the fundamental tool toevaluate the sustainability of next generation biofuels. The LCA litera-ture makes use of the metrics of net energy ratio (NER, defined hereas the ratio of energy consumed to fuel energy produced) and green-house gas (GHG) emissions per unit of energy produced as the function-al units. The results from LCA are highly sensitive to definitions ofsystem boundaries, lifecycle inventories, process efficiencies, and func-tional units [10,13,14]. A variety of researchers have constructed andpresented LCAs of themicroalgae-to-biofuel process, however, inconsis-tencies in system boundaries and high-level process modeling withlarge uncertainties in sub-process modeling have led to a wide rangeof results [15–41]. A survey of the literature shows the NER formicroalgae biofuel production at scale ranges from a low value of 0.2(comparable to petroleum) reported by Luo et al. [31] to 7.8 reportedby Brentner et al. [19] with an extreme value of 1086 reported by Bealet al. [18] based on the extrapolation of small-scale laboratory data.The GHG results from the surveyed literature range between−75.29 g-CO2-eq MJ−1 reported by Batan et al. [17] and 534 g-CO2-eq MJ−1 report-ed by Brentner et al. [19] with other studies reporting values between

117J.C. Quinn et al. / Algal Research 4 (2014) 116–122

these extremes [16–22,28,30,31,33,38,40]. The large variability in theNERand GHGs in previous LCA studies is due to the wide range ofprocesses investigated as well as the assumptions made with regardsystem boundaries, key parameter values, sources of fossil energy, andco-product allocation, which all complicate comparison of resultsamong studies [10,13,14,42–44]. The majority of the studies surveyednow integrates anaerobic digestion as a way to effectively recover nutri-ents and generate on-site heat and energy from the lipid extracted algae(LEA) [20,21,23–25,30,32,37–40]. In the meta-analysis of Liu et al. [30]an anaerobic digester was added to studies that excluded it in an effortto harmonize a baseline scenario and compare results from various re-searchers. The integration of the anaerobic digestion system has beenshown to favorably impact the NER and GHGs.

This study focuses on a LCA founded on the integration of a systemsengineeringmodel informed by experimental andmodeling research to-gether with the Argonne National Laboratory's Greenhouse Gases, Regu-lated Emissions, and Energy USE in Transportation (GREET) model todirectly compare multiple process paths [25,45]. The boundary for theanalysis is such that a comparison to literature and soy-based biofuelcan be transparently performed. The work focuses on the evaluation ofalternative process scenarios to determine process tradeoffs on a systemslevel. The discussion focuses on the environmental impact of changes inthe process parameters on system results, the environmental implica-tions of the integration of an anaerobic digestion system, and comparisonof results from this study to previous environmental impact assessments.

2. Methods

The systems engineering process model serves as the foundation forthe assessment of the various scenarios in terms of environmental im-pact and sustainability. Confidence in the results from these assess-ments is directly related to the validity of the foundational modeling.For this reason care has been taken to ensure that each process modelis independently validated and seamlessly integrated into the systemsmodeling. Validation and proper functionwere assessed through the in-tegration of experimental and literature data and the use of sub-systemsensitivity analysis.

The modularity of the systems engineering model facilitates theevaluation of a variety of alternative processing scenarios for amicroalgae-to-biofuel process. Systems engineering models were inte-grated with the ANL GREET model to evaluate each process in termsof the NER and environmental impact. This research focused on three

Fig. 1. Simulation architecture for the integration of systems engineering model and lifecycle mwell-to-pump.

of the main process steps to produce biofuel from microalgae;microalgae production, lipid extraction, and end-use of LEA. The follow-ing section details the production system and the various assumptionsfor the LCA. Specifically, four scenarios are evaluated; 1) Baseline,2) Improved production, 3) Supercritical CO2 extraction, and 4) Nonutrient recycle, where the LEA is used directly (un-dried) as cattlefeed. In addition, the baseline scenario (50% lipids) is compared to theANL base case (25% lipids) to reveal the effect of lipid concentrationon the NER. The baseline system is modeled to represent a near-termlarge-scale production facility. The simulation architecture is presentedin Fig. 1. The modular construction of the systems engineering modelfacilitates the evaluation of alternative processes on a systems level.

2.1. Microalgae production

A three stage bioreactor system for both growing Nannochloropsissalina and increasing the lipid content is used for the production oflipid rich biomass. Stage I is a low volume, closed bioreactor maintainedwith sufficient levels of all nutrients and is intended to supply a contin-uous clean inoculum for the large-scale facility. Stage II is a high volumeopen raceway design optimized for algal growth. The open racewaysection includes staggered down-flow U-tube configurations placedaround the pond that incorporate the principles of airlift reactors tomove the culture and supply CO2 (details presented in the Supplemen-tary material). Stage III takes the outflow of stage II and imposes thephysical–chemical conditions needed to induce lipid accumulation.The system outlined is designed to achieve high biomass productivitiesand lipid concentrations with improved passive thermal control [46].

2.2. LCA scenario assumptions

The following sections detail the differences between the four LCAscenarios with a summary of the GREET inputs for the various casespresented in Table 1. Amajority of the assumptions for the baseline sce-nario are translated to the other three scenarios in an effort to directlycompare the results of major process changes on a systems level.

2.2.1. Scenario 1: Baseline

2.2.1.1. Growth system. As mentioned above, the production systemmodeled is based on a 3 stage bioreactor system. The energy requiredto move the culture between bioreactors and processing facilities is

odeling, red dotted line represents system boundary of the study and is representative of

Table 1Detailed inputs to LCA for the four scenarios simulated.

Scenario Baseline IP SC-CO2 No AD Units

GrowthWhole microalgae biomass 26.1 26.1 26.1 26.1 MJ kg−1

Lipid-extracted biomass 13.8 13.8 13.8 13.8 MJ kg−1

Oil 50 50 50 50 %Productivity 25 50 25 25 g m−2 d−1

Circulation energy 38 38 38 38 kWh ha−1 d−1

NutrientsUrea 0.018 0.018 0.018 0.1 kg kg-algae−1

Diammonium phosphate 0.027 0.027 0.027 0.053 kg kg-algae−1

De-waterDissolved air flotation

Energy consumption 0.133 0.133 0.133 0.133 kWh kg−1

Coagulant used 10 10 10 10 g kg-algae−1

CentrifugeEnergy 3.29 3.29 3.29 3.29 kWh m−3

Recovery efficiency 95 95 95 95 %

ExtractionPressure homogenization

Efficiency 90 90 – 90 %Energy consumption 0.183 0.183 – 0.183 kWh kg-dry−1

HexaneEfficiency 0.95 0.95 – 0.95Hexane consumption 5.3 5.3 – 5.3 g kg-oil−1

Heat required 4.95 4.95 – 4.95 MJ kg-oil−1

Electricity required 1.94 1.94 – 1.94 MJ kg-oil−1

Nutrient recoveryMethane yield 0.33 0.33 0.33 – m3 kg-VS−1

Biogas CH4 fraction 0.67 0.67 0.67 – Volume basisEnergy required 2.45 2.45 2.45 – MJ kg-digested−1

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assumed to be 4.8 × 10−5 kWh L−1 and the energy to mix the reactorsis 38 kWh ha−1 d−1, approximately 25% less than the energy requiredfor a traditional paddlewheel racewaypond [25]. The productivity of thesystem is assumed to be 25 g m−2 d−1 with a lipid content of 50%based on the systems assumed to be commercially operating [47–51].

2.2.1.2. Nutrients. Microalgae cultivation requires a variety of nutrientsincluding but not limited to nitrogen, phosphorous, CO2, water, andsunlight. Nitrogen in the form of urea and phosphorous in the form ofammonium hydrogen phosphate are supplied to the culture based onthe Redfield ratio of C:N:P of 103:9.8:1 [52]. It is assumed that themicroalgae are comprised of 50% carbon [53]. The CO2 supplied to thesystem was assumed to be co-located with flue gas containing 15%CO2 [54,55]. It is assumed that the CO2 is transported via low pressurepipeline to the facility. Transmission of carbon from the flue gas to theculture is assumed to be done with an efficiency of 85%. A sump depthof 1.5 m provides the mechanism for culture movement and the deliv-ery of a 2% CO2 streamwith details presented in the Supplementaryma-terial [47,56].

2.2.1.3. De-water. The de-water system includes three de-wateringsteps: settling (bioflocculation), dissolved air flotation, and a centri-fuge. Upon harvest, it is assumed that the microalgae are passivelysettled to a density of 10 g L−1 [57]. The slurry is then concentratedto 10% solids, consuming 1.33 × 10−4 kWh gDW−1 via dissolved air flo-tationwith a retention efficiency of 90% [58]. The slurry is then trans-ferred to a centrifuge and concentrated to 20% solids consuming3.29 × 10−3 kWh kg-effluent−1 [59]. It is assumed that the dis-solved air flotation process retains 90% of the microalgae solids andthe centrifuge process retains 95%.

2.2.1.4. Extraction. The system uses a two-step extraction process;pressure homogenization and hexane extraction. The pressurehomogenization is assumed to be 90% efficient and consumes1.83 × 10−4 kWh gDW−1 [48]. This process is followed by a 95% efficient

wet hexane extraction. The process includes solvent recovery with aloss of 0.0053 g-solvent g-oil−1 and requires 5.4 × 10−4 kWh g-oil−1

[29,39,44,50].

2.2.1.5. Nutrient recovery. The baseline systemused an anaerobic digesterto recover nutrients and generate electricity and heat. The following as-sumptions are used, a mass ratio of 0.9 volatile solids (VS) to total solids,methane yield of 0.33 L g-VS−1at a methane fraction of 67%, requiredheat of 5.4 × 10−4 kWh g-digested−1, 1.4 × 10−4 kWh g-digested−1

of electricity, and 2% methane loss [60–64]. Details on the selectedassumptions are presented in the Supplementary material.

2.2.1.6. Displacement credits. The lipid extracted microalgae can be uti-lized in several ways; through digestion for production of heat and en-ergy, as fertilizer, or as cattle feed. Each involves its own particularenergy or GHG credits. The digested solids from the anaerobic digesterare assumed to replace purchased fertilizer with energy and environ-mental credits based on the ANL GREET model [45].

2.2.2. Scenario 2: Improved production (IP)This scenario is based on improved biomass production. The biomass

productivity was increased to 50 g m−2 d−1 with the lipid percentageof the baseline scenariomaintained, 50%. It is assumed that no addition-al energy is required for improved growth relative to Scenario 1. It isnoted that Scenario 2 is feasible but in general not a realistic growthscenario based on the literature and current experimental data[7,53,65,66], however, continued improvements in traditional terrestri-al crop yields indicate this level of production is not unreasonable in thefuture and does not exceed theoretical limits [7,66]. All other assump-tions with regard to the growth process are kept the same. The inputassumptions for Scenario 2 are presented in Table 1.

2.2.3. Scenario 3: Supercritical carbon dioxide extraction (SC-CO2)Supercritical carbon dioxide (SC-CO2) extraction has been used to

extract lipids frommicroalgae at the laboratory scale [67]. Experimental

Fig. 2. ASPEN Plus model of supercritical CO2 extraction.

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data to model the energy requirements for the process at large-scale iscurrently unavailable. An ASPEN Plus model was developed to modelthe energy and efficiency of a SC-CO2 extraction process. The process in-cludes compressing CO2 to high pressure in order to effectively dissolvethe lipids. The process flow simulated in ASPEN Plus is presented inFig. 2. A detailed schematic and description of the process includingpressures and temperatures of each stage is presented in the Supple-mentary material.

The assumptions for Scenario 3 are based on a dry supercritical CO2

extraction process since a wet process is unlikely to work because ofcomplications arising due to high water concentrations [68,69]. It is as-sumed that the CO2 is compressed from atmospheric pressure and tem-perature to 7000 kPa and 300 K at a flow rate of 10 kg h−1. Therequired energy for the extraction of lipids using SC-CO2 includesboth the energy to dry the microalgae and to compress the CO2,0.03 kWh g-oil−1. The extraction efficiency of the process is assumedto be 90%. It is important to note that the effect of using a dry feedstockas feed for the AD is not included in this analysis, i.e., for nutrient recov-ery, it is assumed that the required water to rehydrate the LEA for suffi-cient digestibility will be recovered from the biomass drying stage. TheANL GREET inputs for Scenario 3 are presented in Table 1.

2.2.4. Scenario 4: No nutrient recycle (no AD)Scenarios 1–3 utilize an anaerobic digester to not only recover nutri-

ents but also supply on-site electricity and heat. The no nutrient recyclescenario assumes that the wet lipid extracted microalgae is used to dis-place a current product such as cattle feed [70,71]. The input assump-tions for Scenario 4 are presented in Table 1.

3. Results and discussion

The results for the four scenarios and the baseline results fromFrank et al. [25] are presented in two metrics, net energy ratio (NER)

Table 2Net energy ratio (NER) results for the four scenarios of this study as compared to baselineresults of Frank et al. [25]. NER is defined here as the energy to produce the fuel over theenergy contained in the fuel.

Stage Baseline IP SC-CO2

No AD Frank et al. [25]

Growth and first de-water 0.13 0.10 0.13 0.22 0.23Remaining de-water 0.04 0.04 0.04 0.04 0.08Lipid extraction 0.20 0.20 2.61 0.23 0.24Anaerobic digester 0.11 0.11 0.12 0.00 0.29CO2 delivery 0.01 0.01 0.01 0.02 0.02Anaerobic digester credit −0.33 −0.33 −0.36 0.00 −0.84Conversion 0.17 0.17 0.17 0.17 0.17Transportation and distribution 0.01 0.01 0.01 0.01 0.01Feedstock input 0.34 0.34 0.34 0.34 0.36Total 0.68 0.65 3.07 1.03 0.56

and well-to-pump (WTP) greenhouse gas emissions (GHG). For thisstudy, the NER is defined as the ratio of the net amount of energyused to produce biofuel to the total energy contained in the biofuel,thus a NER of less than unity is desirable. The WTP boundary is definedas the portion of the overall pathway that includes all the steps startingwith microalgae growth through delivery of fuel to the filling station.The remainder of the pathway involves using the fuel in a vehicle andis defined as pump-to-wheels (PTW). For comparison to other studies,the consumption of the fuel in a compression ignition direct injectionengine (PTW) generates approximately 72.0 g-CO2-eq MJ−1 (calcula-tion and assumptions are presented in the Supplementary material).Well-to-wheel results are the combination of WTP and PTW.

3.1. Net energy ratio (NER)

The GREET results for the four scenarios and the baseline resultsfrom Frank et al. [25] are presented on the metric of the NER inTable 2. For comparison purposes, the NER for the production of dieselfrom conventional petroleum is 0.2 [72].

From a technoeconomic standpoint, higher lipid content is desirableas it reduces equipment cost and footprint proportionately. For this rea-son, the baseline of this study assumes a lipid percentage of 50% com-pared to the baseline of Frank et al. [25] of 25%, resulting in thebaseline of this study producingmore lipids which is desirable for a bio-fuel product. Comparing the baseline process to that of Frank et al. [25],the increase in lipid content increases the NER of the system by 20% dueto a decrease in the amount of mass going to the anaerobic digester.This in turn decreases the waste heat and electricity generated as co-products in the co-located downstream processing digester, whichactually leads to an undesirable increase in the NER. This is a critical in-sight regarding the NER portion of the LCA, namely, that for achieving alowerNER, themost important design consideration is having anoverallprocess that captures as much of the energy content as possible that iscontained in both the lipids and the LEA rather than focusing only onlipid content. Unfortunately, using this approach to lower the NER islikely to have a negative effect on process economics, related tothe lower lipid content of the algae. Doubling the productivity(Scenario 2) has a positive effect on the NER but only decreases theNER by 5% as compared to the Baseline scenario. This illustrates thatincreasing the biomass yield has minimal impacts on the NER.

The high energy consumption centered on the SC-CO2 extraction il-lustrates the impracticality of an extraction system that consumes0.03 kWh g-oil−1. The majority of the energy required in this scenariois involved in the drying of the biomass prior to extraction. A varietyof researchers have confirmed the need to develop wet extractiontechnologies in order for the process to be environmentally andeconomically favorable [17,25,30].

The elimination of the anaerobic digester illustrates the impact ofon-site heat and energy production and nutrient recycle from the LEA

120 J.C. Quinn et al. / Algal Research 4 (2014) 116–122

on the NER. In this scenario, the NER is double that of the baseline andthese results confirm the importance and need to fully understand theperformance of an anaerobic system operated on LEA. In terms of ener-gy analysis, the performance of this sub-system is as important as un-derstanding the growth performance of the microalgae-to-biofuelprocess in terms of quantifying the energy consumption and environ-mental impact on a systems level.

3.2. Greenhouse gas emissions

The GHG results for the various scenarios are presented in Table 3 inunits of net grams of carbon dioxide equivalent (CO2-eq) per mega jouleof biofuel produced (MJ). Carbon dioxide equivalent includes carbon di-oxide emissions, methane emissions based on an IntergovernmentalPanel on Climate Change (IPCC) standard 100 year impact of 25, and ni-trous oxide emissions based on an IPCC standard 100 year impact of 298[73]. The GHG results presented are for aWTP boundary as illustrated inTable 3 and it includes transportation and distribution (T&D) of fuel tothe pump.

The impact of the anaerobic digester is also illustrated by the GHG re-sults. The baseline, improved growth, and Frank et al. [25] all take advan-tage of a large credit for biogas generation and combustion from theanaerobic digester for onsite heat and energy combinedwith nutrient re-cycle. Focusing on the credit that is associated with the anaerobic digest-er, an improved growth configuration where lipid percentage in thebiomass is increased will decrease the amount of biomass fed to the an-aerobic digester and in turn decreases the amount of onsite heat and en-ergy that can be generated. This corresponds to a smaller GHG credit dueto a decrease in biogas production leading to an increase in electrical con-sumption. Scenario 4 which does not utilize the anaerobic digester, theGHG emissions are close to the breakeven point illustrating the integralpart the anaerobic digester has on the overall environmental impact.

In addition to the “strain-to-pump” analysis designed to be equiva-lent to the well-to-pump boundary, the results from this study can beexpanded to include the combustion of the fuel for a “strain-to-wheel”LCA boundary, which is equivalent to the traditional well-to-wheel forconventional diesel. The “strain-to-wheel” analysis includes all stagesof “strain-to-pump” as well as the combustion of fuel in transportationvehicles. For this expanded analysis it is assumed that soybean-derived and microalgae-based diesel fuels are used in 100% pure formin compression–ignition, direct-injection (CIDI) engine vehicles. Dueto the lack of emissions data from the combustion of microalgae-based biofuel, it was assumed that the fuel economy and emissions

Table 3GHG emissions for the 4 scenarios compared to Frank et al. [25]. Results are presented ing CO2-eq MJ−1.

Stage Baseline IP SC-CO2 No AD Franket al.

Algae growth and 1stde-watering

13.9 10.3 21.7 32.6 30.8

Remaining de-watering 5.2 5.0 8.4 8.1 9.4Transport of algae biomass toextraction

0.0 0.0 0.0 0.0 0.0

Oil extraction 20.6 20.0 542.6 29.0 25.6Anaerobic digester 13.1 12.9 18.7 0.0 36.0Biogas clean-up and recoveryCO2 transfer to pond

2.1 2.0 3.4 0.0 4.8

Biomass and biogas combustion −24.7 −24.7 −26.8 0.0 −63.6Soil application of AD residue 3.0 3.0 3.1 0.0 5.7Displacement credit for ADresidue as fertilizer

−3.6 −3.6 −3.8 0.0 −7.2

Biodiesel transportation anddistribution

0.7 0.7 0.7 0.7 0.7

Feedstock 2.1 2.0 2.8 3.4 3.6Biofuel transport 1.7 1.7 1.7 1.7 1.7CO2 growth credit −75.8 −75.8 −75.8 −75.8 −75.8Total −41.7 −46.5 496.7 −0.3 −28.4

from soy- and microalgae-based biofuels in CIDI vehicles are the same.The emissions associated with the combustion of the fuel were deter-mined to be 72.0 g CO2-eq MJ−1, making the baseline pump-to-wheelGHG emissions to 31.3 g CO2-eq MJ−1 (details on calculations are pre-sented in the Supplementary material). This result is 3 times lowerthan conventional diesel, 93.08 g CO2-eq MJ−1.

3.3. Comparison to literature

A variety of literature LCAs have been performed with the NER re-sults ranging from microalgae-based biofuels being comparable to tra-ditional fuels to worse by two orders of magnitude. Recent meta-analysis has made efforts to decrease the variability by using modelingtechniques that harmonize boundaries and processes. The baseline pro-cess model for the recent meta-analysis included the integration of ananaerobic digestion unit [30]. A survey of the literature shows that theintegration of an anaerobic digestion unit positively impacts the NERof the microalgae-to-biofuel process, Fig. 3. The NERs presented inFig. 3 are limited to the baseline or low values of the literature surveyeddue to the majority of the articles reporting a range of the NER due toprocess sensitivity. The NER for systems that do not include an anaero-bic digester can have favorable energy results, however only one of thestudies surveyed that integrate an anaerobic digester exceeds a NER of1. The results from the literature are consistent with the results fromthis study; integration of an anaerobic digester positively impacts theNER of the microalgae-to-biofuel system.

This study through the integration of engineering process modelsvalidated through experimental and modeling research illustrates thesystems level effects of an anaerobic digester on the overall energybalance and GHGs for a microalgae-to-biofuel process. The processsensitivity analysis demonstrated the importance of a wet extractionprocess and the need to effectively recycle LEA. The study shows thathigher lipid content in the biomass is detrimental to the NER due to areduction in the total energy that can be captured by the anaerobic di-gester. Systems level assessments show the interconnection between

Fig. 3. Net energy ratio (NER) for previous literature studies and this study highlightingthe effects of the integration of anaerobic digestion (AD). NER is defined here as the energyover the produced energy, NER b 1 is desirable.

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process steps which can dramatically impact the environmental impactof the system.

Acknowledgment

The authors gratefully acknowledge financial support from the De-partment of Defense Air Force Research Laboratory, FA-8650-11-c-2127.

Appendix A. Supplementary material

Supplementary material to this article can be found online at http://dx.doi.org/10.1016/j.algal.2013.11.002.

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