The Energy Return on Investment for Algal Biocrude

24 Pages·2012·2.13 MB·English

The Energy Return on Investment for Algal
Biocrude: Results for a Research Production
Facility
Colin M. Beal, Robert E. Hebner,
Michael E. Webber, Rodney S. Ruoff &
A. Frank Seibert
BioEnergy Research

ISSN 1939-1234
Volume 5
Number 2

Bioenerg. Res. (2012) 5:341-362
DOI 10.1007/s12155-011-9128-4
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Author's personal copy
Bioenerg.Res.(2012)5:341–362
DOI10.1007/s12155-011-9128-4
The Energy Return on Investment for Algal Biocrude:
Results for a Research Production Facility
Colin M. Beal&Robert E. Hebner&
MichaelE.Webber&RodneyS.Ruoff&A.FrankSeibert
Publishedonline:13July2011
#SpringerScience+BusinessMedia,LLC.2011
Abstract This study is an experimental determination of investments. The EROI for the production process evalu-
the energy return on investment (EROI) for algal biocrude ated here was significantly less than 1, however, the
production at a research facility at the University of Texas majority of the energy consumption resulted from non-
at Austin (UT). During the period of this assessment, algae optimized growth conditions. While the experimental
were grown at several cultivation scales and processed results do not represent an expected typical case EROI for
using centrifugation for harvesting, electromechanical cell algal fuels, the approach and end-to-end experimental
lysing,andamicroporoushollowfibermembranecontactor determination of the different inputs and outputs provides
for lipid separation. The separated algal lipids represent a a useful outline of the important parameters to consider in
biocrude product that could be refined into fuel and the such an analysis. The Experimental Case results are the
post-extraction biomass could be converted to methane. To first known experimental energy balance for an integrated
determine the EROI, a second-order analysis was con- algalbiocrudeproductionfacility,andassuch,areexpected
ducted,whichincludesdirectandindirectenergyflows,but tobehelpfulforsettingresearchanddevelopmentpriorities.
does not include energy expenses associated with capital In addition to the Experimental Case (based on direct
measurements), three analytical cases were considered in
this work: (1) a Reduced (Inputs) Case, (2) a Highly
: : : Productive Case, and (3) a Literature Model. TheReduced
C.M.Beal R.E.Hebner M.E.Webber R.S.Ruoff
(Inputs) Case and the Highly Productive Case speculate the
DepartmentofMechanicalEngineering,
energyuseforasimilarsysteminanimproved,commercial-
TheUniversityofTexasatAustin,
Austin,TX,USA scale production setting. The Literature Model is populated
with relevant data that have previously been reported in the
M.E.Webber
literature.FortheExperimentalCase,ReducedCase,Highly
TheCenterforInternationalEnergy&EnvironmentalPolicy,
Productive Case, andLiterature Model , the estimated
TheUniversityofTexasatAustin,
Austin,TX,USA second-order EROI was 9.2×10−4, 0.074, 0.22, and 0.35,
respectively.Theseresultsweredominatedbygrowthinputs
R.S.Ruoff
(96%, 89%, 87%, and 61% of the total energy requirement,
TheTexasMaterialsInstitute,TheUniversityofTexasatAustin,
respectively). Furthermore, the EROI was adjusted using
Austin,TX,USA
quality factors thatwere calculated according tothe price of
A.F.Seibert each input, yielding a quality-adjusted EROI that parallels a
CenterforEnergyandEnvironmentalResources,
partial financial return on investment analysis. For the
SeparationsResearchGroup,TheUniversityofTexasatAustin,
Experimental Case, the Reduced Case, and the Highly
Austin,TX78712,USA
Productive Case, the quality-adjusted EROI was 9.2×10−5,
:
C.M.Beal R.E.Hebner(*) 0.013, and 0.36, respectively.
CenterforElectromechanics,TheUniversityofTexasatAustin,
1UniversityStationC2200,
Keywords Algae.Energy return on investment.Energy
Austin,TX78712,USA
e-mail:[email protected] balance.Net energy ratio.Biofuel.Biodiesel
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342 Bioenerg.Res.(2012)5:341–362
Abbreviations 8 Volume pumped (in units of liters)
Products ΔP Pressure drop (in units of pascals)
BO Bio-oil η Pump efficiency
BMF Biomass fuel ρ Density (in units of grams per liter)
BF Biofuel Δz Change in elevation (in units of meters)
BS Biomass in slurry f Friction factor
BC Biocrude K Loss coefficient
L
GM Grown mass U Flow velocity (in units of meters per second)
HM Harvested mass Q Heat (in units of joules)
LM Lysed mass c Specific heat capacity (in units of joules per
p
kilogram kelvin)
Processes ΔT Temperature difference (in units of kelvin or
G Growth
degrees Celsius)
P Processing g Gravity constant (9.81 m/s2)
R Refining
L Pipe length (in units of meters)
H Harvesting
D Pipe diameter (in units of meters)
CL Cell lysing
NEC Net energy content (in units of joules per liter or
S Separations
joules per liter per day)
Efficiency
proc Processing Accents
ref Refining Tilde denotes an input for a processing step
harv Harvesting Apostrophe indicates units of joules per liter of
cellys Cell lysing processed volume
sep Separations Invertedapostropheindicatesunitsofjoulesperliterof
processed volume per day of cultivation
Composition
LF Lipid fraction
ULF Useful lipid fraction
Introduction
NLF Neutral lipid fraction
Nomenclature Algae areapotential biofuelfeedstockthat havereceiveda
EROI Energy return on investment great deal of research interest. Theoretically, algae are
FROI Financial return on investment promising as feedstock because they grow rapidly, do not
P Productivity (in units of grams per liter per day) require fresh water or arable land, and, in some cases, can
M Mass (in units of grams) produce large amounts of energy products (e.g., lipids).
V Velocity (in units of meters per second) These potential advantages have been discussed at length
t Cultivation time (in units of days) elsewhere [1–5]. Practically, however, algal biofuel pro-
c
’ Efficiency duction has proven to be quite challenging. One way to
E Energy (in units of joules) evaluate the production of algal biofuels is to calculate the
ED Direct energy flows (in units of joules) energy return on (energy) investment (EROI), which is
EI Indirect energy flows (in units of joules) similar to the net energy ratio (NER), and can be used to
O Indirect energy (i.e. material) output assessthefeasibility andsustainabilityofanenergysource.
(in units of kilograms) In brief, the EROI is the amount of energy produced
υ Energy equivalent of indirect energy outputs divided by the amount of energy required for that
(in joules per kilogram) production, and it has been used to characterize many
I Indirect energy (i.e., material) input resources. For example, the EROI for production of
(in units of kilograms) conventional oil and gas, coal, wind energy, and corn
γ Energy equivalent of indirect energy inputs ethanol has been estimated to be ∼15, ∼80, ∼19, and ∼1,
(in units of joules per kilogram) respectively [6–9]. For algal fuels to be produced commer-
MP Material price (in units of dollars per kilogram) cially,theEROI mustbecompetitivewiththoseforcurrent
EP Energy price (in units of dollars per joule) energy sources. Similarly, the financial return on invest-
QF Quality factor ment(FROI)foralgalfuelsmustbecompetitivewiththose
v Energy content (in units of joules per kilogram) for current energy sources. The relationship between the
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Bioenerg.Res.(2012)5:341–362 343
EROI and FROI is considered in this study, and character- operation).Thegroupoperatedinabatchprocessingmode,
ized more thoroughly elsewhere [10, 11]. allowing continuous operation of most of the processing
When calculating the energy balance for algal biofuel, steps, albeit for relatively short times. To date, nearly 20
researchersareleftwithtwochoices:(1)tocalculateenergy large-scale batches have been completed (with processed
flows for theoretical systems, which risk incorporating volumes of ∼900–4,000 L per batch).
unrealistic assumptions, or (2) to characterize production The Reduced (Inputs) Case presents speculated energy
basedonresearch-scaleprocesses,whichareoftenknowna consumption values for the operation of a similar produc-
priori to be uneconomical. In this study, both approaches tion pathway at commercial scale, while yielding the same
are explored. Several studies have evaluated the energy energy outputs as obtained in the experiments. The Highly
requirements for growing algae [2, 4, 12–20] and many Productive Case uses similar assumptions for the energy
have also considered the energy required to process algae inputs as the Reduced Case and assumes greater energy
into a commercial product (i.e., food or fuel) [4, 13, 15– output productivity. In addition, the Literature Model
21]. Many of these analyses rely on rough estimates and provides an estimate for the EROI of algal biocrude based
sometimes omit necessary inputs because there is no on data that has been reported in the literature. In this way,
commercial algal biofuel industry to serve as a reference. the Reduced Case is grounded on one side by the sub-
This work describes initial attempts at a clearly defined optimal experimental data and on the other side by the
model for the second-order EROI of algal fuels (which Highly Productive Case and the Literature Model, which
includes direct and indirect energy inputs) and the use of are largely comprised of theoretical data.
end-to-end experimental data to populate the model.
Thescopeofthisstudyislimitedtoevaluatingoperating
energy expenses (including direct and indirect energy Methods and Materials
flows, but omitting capital energy expenses) according to
theEROIframeworkprovidedbyMulderandHagens[22]. Production Pathway
A quality-adjusted EROI value is also presented, which
considers the impact of using high quality fuels (i.e., high Thereareseveralenergycarriersthatcanbeproducedfrom
value fuels, mainly electricity) for production of lower algae, including renewable diesel (such as biodiesel from
quality fuels (i.e., lower value fuels, bio-oil and methane). lipids), ethanol (from carbohydrates), hydrogen produced
The experimental results reported in this study are not photobiologically, methane (via anaerobic digestion or
representative ofacommercial-scale algalbiocrudefacility. gasification), and electricity via direct combustion [17,
Such a facility does not yet exist. Moreover, it is unlikely 29–36]. Biodiesel is the most commonly studied algal
there will be published information on commercial pro- biofuel, and can be produced by transesterification of algal
cesses until the industry matures, as this information is lipids [33]. However, additional refining technologies exist
mostly proprietary. The value of this study is to utilize a that can produce a range of refined fuels from lipids
functional research facility to develop the experimental depending on the lipid composition (e.g., hydrocracking
approach for determining the EROI for algal biocrude [37] and gasification). Algal lipids include neutral lipids
production. This type of analysis will be important for the and polar lipids and the proportion of each type is highly
algal fuels industry, as it has been for current biofuel variable [1, 2, 38, 39]. As a result, it is not clear what
industries [8, 23–28]. It is expected that the EROI will be refining processes will be used on an industrial scale. With
improvedforoptimizedgrowthconditions,refinedprocess- this in mind, the experiments in this study measured the
ing methods, and with the application of future technology energy requirements associated with producing biocrude
(and biotechnology) improvements. (i.e., algal lipids), but do not include the energy associated
The experimental data for producing algal lipids (i.e., with upgrading the biocrude into a refined fuel product. In
biocrude) were acquired during processing of five large- other words, this is a “strain-to-refinery door” analysis.
e
scale batches at the University of Texas (UT; with a total However, the energy requirement of refining, noted as E ,
R
processedvolumeofroughly7,600L),whereoutdooralgal will be included in the analysis in symbolic notation
growthwasintegratedwithseveralcriticalprocessingsteps. (accordingtoaconventionestablishedinapriorpublication
The research focus is on processing; growth is done to [33]) and estimated values will be used when necessary.
provide material to process. The growth facilities at UT Figure 1 presents the production pathway used at UTin
werebuilttobalancecapitalcostswithoperationalcostsfor this investigation. In this approach, algae were grown in
low-volume production on a research budget. Consequent- outdoor“raceway”ponds(∼0.2mdeep), whicharesimilar
ly,thegrowthprocessincludedmanyinefficienttechniques to those discussed in previous studies [2–4, 40] and the
(e.g., artificial lighting, oversized pumps, etc.) that were ponds were inoculated from small-scale bioreactors. The
appropriate for a research setting (but not a commercial diversityofexistinggrowthapproachesandtheresultsofthis
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344 Bioenerg.Res.(2012)5:341–362
Fig.1 Thealgalbiocrude
productionpathwayusedinthis
investigation.Duetorelatively
smallvolumes,theseprocessing
stepsareconductedinbatch
mode,andthestepsbetween
growthandrefininglastabout
3days.*Wetbiomasswillrequire
additionalprocessing/refining
workunderscorethefactthatthisapproach,whilequiteuseful crude) and biomass fuel (produced from the biomass
forresearch,willlikelybemodifiedforcommercialapplica- slurry). Thus, the direct energy output includes the bio-oil
tion. The algae were concentrated by a factor of ∼65 into a energy, ED , and the biomass fuel energy, ED , as,
BO BMF
slurry(i.e.,harvesting)usingcentrifugationandthenelectro-
ED ¼ED þED ð2Þ
mechanicallylysedbyexposuretoelectricalpulses.Afterthe out BO BMF
cells were lysed, a microporous hollow fiber membrane
Ifthebiomassisusedtoproducenon-energyproducts(e.g.,
contactor (MHF contactor) was used to separate the lipids
protein, nutritional supplements, or cosmetics), then it
fromthebiomass.Thelipidsconstituteabiocrudeproductthat
could be represented as an indirect energy flow. In Eq. 1,
can be upgraded to a refined fuel. The biomass also has the
indirect energy flows include material inputs that contain
potentialtobeusedtoproducefuelsorchemicals,andforthis
embedded energy (e.g., the embedded energy in nitrogen
study, a thermochemical conversion process is modeled that
fertilizer) and material outputs. Specifically, the quantity of
produces methane from the post-extraction biomass. Energy
the kth non-energy input is I and the per-unit energy
k
requiredtodistributetherefinedfuel(s)isnotincluded. equivalent value for that input is denoted as γ . Similarly,
k
the quantity of the jth non-energy output is O and the per-
j
Energy Return on Investment Framework
unitenergyequivalentvalueforthatoutputisdenotedasv.
j
However,inthisstudy,therearenoindirectenergyoutputs.
The EROI analysis used in this study is based on the
A quality-adjusted EROI (analogous to a partial FROI
framework provided by Mulder and Hagens [22].
[10,41])wasalsodeterminedforallofthecasesexceptfor
Specifically, the second-order EROI model has been
the Literature Model by multiplying each of the input and
adopted (cf. Fig. 2 in [22]), which accounts for direct
output flows by a corresponding quality factor. For energy
energyflowsaswellasindirectenergyflows,asshownin
flows,thequalityfactors(QF)werecalculatedaccordingto
Eq. 1. The process specific nomenclature in this study is
the energy price (EP), which is the price of each energy
based on the framework provided by Beal et al. [33]
sourceperjoule,whichcorrelatestherelativevalueofeach
P
ED þ vo fuel [42]. Setting coal as the standard with a quality factor
EROI¼ out P j j j ð1Þ of1($1.4/GJ,$1.5/MMBtu),thequalityfactorsusedinthis
ED þ g I
in k k k study were: electricity 19.5 ($27.8/GJ, ¢10/kWh), petro-
leum 14.5 ($20.6/GJ, $2.50/gal), and natural gas 2.7 ($3.8/
Direct energy flows include electricity and fuel con- GJ, $4/MMBtu) [43]. Methane was assigned the quality
sumed during production (ED ) and the biofuel produced factor of natural gas and the bio-oil was assigned the
in
(ED ). In this study, the biofuel products (which are not quality factor of petroleum. For material inputs, the quality
out
actually produced) include bio-oil (produced from bio- factor was determined according to Eq. 3,
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Bioenerg.Res.(2012)5:341–362 345
Fig.2 Materialandenergy
flowsforbiocrudeproductionat
UTaremarkedasarrows
crossingthesystemboundary
the energy consumed in the smaller growth volume was
P ½$=kg(cid:3)
QF¼ (cid:2) ð3Þ allocatedbetweenthetwogrowthvolumesaccordingtothe
EEIEP ½MJ=kg(cid:3)I½$=MJ(cid:3)
coal coal percentage of the smaller volume that was transferred. The
Where P is the price (in $/kg), EE is the energy equivalent batches,hereafter referred toby batch numbers1–5,varied
(withunitsofMJ/kg),andEP istheenergypriceforcoal between 947 and 1,942 L of growth volume processed and
coal
($1.4/GJ). By using quality factors that are based on price, were all processed between May and July, 2010. The
the quality-adjusted EROI analysis is equivalent to the average cultivation time (from inoculation in the airlift
partial FROI analysis that is calculated using the same reactors until harvesting from the ponds) was 123 days, on
inputs and outputs (i.e., excluding capital expenses, labor average.
costs, regulatory fees, etc.) [10].
Growth
Experimental Analysis
In all stages, the growth media were prepared with Instant
Figure 2 displays the input and output products of algal Oceansaltsatasalinityof∼15g/L,andtheconsumptionof
biocrudeproductionatUT.Detaileddescriptionsofalldata salts,nutrients,water,andantibioticswasrecorded.Thefirst
collectionanduncertaintyanalysiscanbefoundin[10](cf. airlift bioreactor was inoculated from flasks on January 26,
Chapter 4, Appendix 4A and Appendix 4B of [10]). The 2010 and the energy consumed for the flask growth stage
alga processed in these batches was a marine species of was neglected. Seven indoor, airlift bioreactors (L1–L7)
Chlorella (KAS 603, provided by Kuehnle AgroSystems, wereusedtogrowthealgaeandweresuppliedwithartificial
Inc.) andwasgrowninfour differentgrowthstages: flasks, lighting (multiple 54 W, Hg bulbs) for 12 h per day. The
airlift photobioreactors, greenhouse tanks, and covered electricity consumption for lighting was measured with
raceway ponds (cf. Fig. 3). In general, the larger growth energy meters and secondary room lighting was neglected.
volumeswereinoculatedfromthesmallergrowthvolumes, The bioreactors were maintained at about 24°C and a CO /
2
and all of the algae transfers are illustrated in a flow air mixture (average of 1.0% CO ) was bubbled into the
2
diagram in [10] (cf. Appendix 4A in [10]). Energy bioreactors continuously (the out-gassed CO /air mixture
2
consumption for growth and processing equipment was from the top of the reactors was 0.72% CO , on average).
2
either measured with energy meters or estimated according The CO /air flow rate and the percentage of CO in the
2 2
to the manufacturer specifications. When algae were mixture were recorded daily for each reactor. The CO /air
2
transferred from a smaller growth volume to a larger one, mixture was provided by mixing CO from a gas tank with
2
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346 Bioenerg.Res.(2012)5:341–362
Fig.3 Algaeweregrowninfourgrowthstages:1)flasks2)sevenindoorphotobioreactors(L1–L4shown,~100Leach)3)fourgreenhouse
tanks(G1andG2shown,~1000Leach)and4)twocoveredoutdoorponds(shownwithoutthecover,~2400Leach)
compressed air from a general-use shop compressor. There- Harvesting
fore, the compressor power for the airlift reactors could not
be measured, and was estimated from the compressor data Thealgaewerepumpedfromthepondsinto1,200-Ltotesand
obtained for the greenhouse tanks and outdoor pond. transported to the centrifuge facility by a propane powered
Four greenhouse tanks (G1–G4, about 0.25–0.50 m vehicle. Thepumpingenergywas measuredusing anenergy
deep and nominally 946 L each) were periodically meter and the transportation energy was estimated roughly
inoculated from the airlift bioreactors, and then used to (0.26milesroundtripand10miles/gallonofpropane).During
inoculate the ponds (P1 and P2, about 0.2 m deep and centrifugation,energy wasconsumedbyan algaefeed pump
nominally 2,400 L each). Inoculations were made at andthecentrifuge.OnefeedpumpwasusedforBatch1and
irregular intervals ranging from days to months (cf. anotherpumpwasusedforBatches2–5.Thefirstwasahard-
Appendix 4A of [10]). A mixture of CO and air was wired 220 V pump and the second was a 120 V pump. The
2
bubbled into the greenhouse tanks and ponds, and was energyconsumptionforthefirstpumpwasestimatedaccord-
supplied by a compressor and a CO tank (different than ingtothemanufacturerspecifications(0.7A,215V,and0.9
2
those used for the airlift reactors). The total CO flow rate power factor) and the energy consumption for the second
2
for all of the greenhouse tanks and ponds was measured pumpwasmeasureddirectly.Thecentrifugewasoperatedona
daily, and allocated by relative volume. Two compressors variable frequency drive, which controlled the power con-
wereused:theenergyconsumptionforthefirstcompressor sumption(continuousat2.48A,215V,and0.9powerfactor).
wasmeasureddirectlywithanenergymeterandthatforthe On average, centrifugation achieved a 65× concentration of
second compressor (used for only 8% of the cultivation algaldryweightpervolumefrom0.26to16.7g/L.
time)wascalculatedbymeasuringthecurrent,voltage,and
duty cycle. In addition, the greenhouse contains two fans Cell Lysing
that are activated by a thermostat (set to 32.2°C), and the
electricity consumed by these fans, which varied according The electromechanical celllysingprocess was conducted by
to the ambient temperature, was also measured. A pump applyingshortpulsesofstrongelectricfieldstoalgaeflowing
requiringapproximately0.8kJ/Lwasusedtotransferalgae througha20-mLtest-cellthatconsistsoftwoelectrodes.Each
from the tanks to the ponds. The energy required for electrical pulse was applied by the discharge of several
transfers from the indoor airlift bioreactors to the green- parallel capacitors that are charged on a three-phase, 480 V,
house tanks was also estimated to be 0.8 kJ/L. Confer [10] AC circuit. The electricity consumed during each pulse was
for more details. determinedtobe480J,onaverage(cf.[10]).
The final growth stage was in outdoor, covered,
raceway ponds that can hold approximately 2,400 L Lipid Separation (Extraction)
each. The ponds were covered with a plastic liner to
reduce evaporation and contamination, and circulation A microporous hollow fiber membrane contactor (MHF
was accomplished by a pump that was operated 24 h per contactor)wasusedtoseparatethealgallipidsfromtheother
day (requiring ∼1,130 W). biomass into heptane. Due to the specific research that was
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Bioenerg.Res.(2012)5:341–362 347
beingconducted,theseparationwasconductedbycyclingthe consumed. The energy consumption for lysing is modeled
algae and heptane through the MHF contactor for the time using the same process as the experiments, but with a more
equivalent of three passes. Then, the contactor was washed efficientpowersupplyanda properlysizedpump.Asinthe
withfreshsolvent(heptane),andthewashsolventwasadded experiments, a MHF contactor separation process and
totheinitialsolventvolume.Thealgallipidswererecovered subsequent distillation are used for the Reduced Case and
via distillation, and most of the heptane was recovered as Highly Productive Case. However, by modeling proper
distillate. On average, 1.6 L of solvent was consumed per equipment size and assuming continuous operation, the
batch (equivalent to 0.98 mL of solvent per L of growth energy consumed during separations in these cases is
volume processed). However, the MHF contactor retains significantly less than that of the experiments. With proper
about 1.5 L of solvent, and due to batch processing, this design,asinglepumpcanbeusedtomovealgalconcentrate
solventwaslosttoevaporation.Incontinuousoperation,the from harvesting, through lysing, and through the lipid
solvent consumption would be much lower (cf. Reduced separation contactor. Thus, only one additional pump is
Case). The electricity consumed during the separation required for passing solvent through the contactor.
processes was either measured directly with energy meters
orestimatedfromtheequipmentmanufacturerspecifications.
The energy-consuming equipment included: (1) an algae Results
feedpumpforthecontactor,(2)asolventfeedpumpforthe
contactor, (3) a distillation peristaltic solvent/oil feed pump, Summary of Batches
(4)adistillationvacuumpump,and(5)twoelectricalheaters
fordistillation.Inaddition,theamountofchilledwaterused Table1 summarizes processing efficiency data obtained for
tocondensetheheptanedistillatewasmeasured.ForBatch3 eachofthefivebatchesinthisstudy.Tocalculatethesedata,
only, the post-extraction biomass was re-extracted (half of samples were collected during processing of each batch
which was re-lysed), yielding additional oil, and accruing using a methodology that has been described previously
additional energy inputs. Thus, the data reported for the [10]. The terminology and nomenclature that is used has
lysing and extraction of Batch 3 include contributions from been defined previously by Beal et al. [33]. The efficiencies
the re-lysing and re-extraction. arecalculatedasthemassratiooftheoutputofaproduction
step divided by the input for that step (e.g., the separations
Reduced Case and Highly Productive Case efficiency is the mass of biocrude divided by the mass of
lysed algal biomass. The neutral lipid fraction is embedded
The purpose of the Reduced (Inputs) Case and Highly inthisefficiency).Therefore,thesetermsdonotrepresentthe
Productive Case is to provide a more realistic model for effectiveness of each step (except for the harvesting
operatingenergyexpensesthatareexpectedinacontinuous, efficiency, which also represents the harvesting effective-
commercial-scaleproductionfacility. The energyoutputs for ness).Similarly,theoverallprocessingefficiencyisthemass
theReducedCaseareassumedtobethesameasthoseinthe of biocrude divided by the grown mass and incorporates
experiments, while the Highly Productive Case assumed a each of the individual processing efficiencies. Neutral lipid
greaterbiomassproductivity(0.08g/Ld,∼16g/m2d)anda recovery is the percentage of neutral lipids detected in the
higher neutral lipid fraction (30%), which yields a greater initial biomass (as determined by HPLC analysis (Poenie,
energy output. The energy associated with capital expendi- personal communication), data not shown) that were
tures required to achieve these cases is not considered and recovered as biocrude. There are several variables that
the ability to achieve all of these conditions is speculative. influence the neutral lipid recovery, including each process-
The Reduced Case and Highly Productive Case models use ing efficiency and changes in the neutral lipid composition
the same basic production pathway that was used for the throughout processing [10, 44].
experimental results (cf. Fig. 2), but substitute bioreactors
for growth and an advanced flocculation technique in place Experimental Energy Flow Results
of centrifugation. Several modifications are implemented to
improve energy efficiency. Table2liststhedataobtainedforthegrowthandprocessing
In the Reduced Case and Highly Productive Case, algal of Batches 1–5. All of the indirect energy inputs were
cultivation is envisioned to be accomplished in a closed, converted to energy values using the energy equivalent per
outdoor reactor (which does not require volume transfers) unitofeachindirectinput(e.g.,theenergyequivalentofurea
that is mixed by rotary stirring (rather than pumping). is 26.30 MJ//kg). Since the volume that was processed for
Harvesting is modeled as an advanced flocculation tech- eachbatch wasdifferent,the dataare normalized perliterof
nique. Energy is consumed by a pump to move the growth growth volume processed and reported in units of kJ/L.
volume to the harvesting facility and by flocculants that are Table 3 lists the average value for each input and output
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348 Bioenerg.Res.(2012)5:341–362
Table1 Processingsummaryforthefivebatchesinthisstudy
Batch Proc. Algal Grown Grown Neutral Harv. Lysing Sep BC Biomass Proc. BCprod.a Neut.
vol. conc. mass massprod.a lipidfractionb effic. effic. effic. mass M (g) effic. P lipid
BS BC
V (L) μ(g/L) M P NLF ’ (−) ’ (−) ’ (−) M (g) ’ (−) (mg/L-d) recoveryb
P GM GM harv cellys sep BC proc
(g) (mg/L-d) (%DCW) (%)
1 947 0.27 260 2.76 0.015 1.19c 0.65c 0.007 1.44 154 0.006 0.015 37%
2 974 0.25 244 2.55 0.019 0.90 0.92 0.014 2.88 138 0.012 0.030 62%
3 1891 0.25 470 2.12 0.011 0.97 0.94 0.012 4.96 301 0.011 0.022 96%
4 1893 0.22 425d 3.05 0.026 -d 0.95 0.011 4.44 310 0.010 0.018 40%
5 1942 0.29 570 1.70 NA 0.88 0.88 0.005 2.15 378 0.004 0.006 NA
Ave 1529 0.26 394 2.17 0.018 0.92c 0.92c 0.010 3.17 256 0.008 0.02 59%
Stdev 520 0.02 140 0.47 0.006 0.05 0.03 0.004 1.49 105 0.004 0.01 27%
aThecultivationtimeandgrowthvolumeusedforthegrownmassproductivityandbiocrudeproductivitycalculationsareroughestimates
bTheinitialneutrallipidfractionwasestimatedbyHPLCanalysisandincludeslipidspecieswithpolaritylessthanorequaltoMAG(e.g.,LCH,
TAG,andDAG)[45].HPLCdataisnotreported
cThe post-harvesting algal concentration measured for Batch 1 is high, resulting in a harvesting efficiency greater than 1 and a low lysing
efficiency.TheseeffectsareoffsetintheprocessingefficiencycalculationforBatch1andthesedatawereomittedfromtheaverage
dThedatareportedwasmeasuredafterharvesting,andthereforetheharvestingefficiencycannotbedetermined
across the five batches. In addition, the percentage of the was accomplished by an oversized pump (∼1,130 W,
totalenergyconsumption/production,theuncertainty,andthe operated 24 h/day and 7 days/week); the use of a paddle-
standard deviation are listed. wheel or pump duty cycle would significantly reduce this
There are three types of uncertainties associated with consumption.Artificiallightingoftheairliftphotobioreactors
using the experimental data presented in this study for was used to enable stable growth conditions, but could be
evaluating the EROI of algal biofuels in general: replaced by the use of sunlight. Air compression require-
measurement error, artifacts associated with sub-optimal mentsandCO consumptioncouldbereducedbyemploying
2
research-scale production, and batch-to-batch variations. more efficient CO delivery methods (to improve CO
2 2
A detailed error analysis is provided in [10] that uptake rates, therefore reducing the amount of CO /air
2
addresses measurement error, and the uncertainty results needed) and using an appropriately sized compressor. The
are tabulated for each input and output in Tables 2 and 3. amount of water used for each batch was calculated to be
The Reduced Case and Highly Productive Case are 1.91Lforeveryliterprocessed(duetoevaporationfromthe
provided below in an attempt to address research-scale growth volumes). About 98% of the water processed is
artifacts by estimating the EROI for an optimal commer- recovered after harvesting and could be recycled, but would
cial-scale operation of a similar production pathway. likely require additional treatment. Although no recycling is
Finally, batch-to-batch variations in the growth and includedinthisstudy,if100%recyclingwereaccomplished,
processing methods are characterized by the standard the water consumption would be reduced to 0.91 L/L
deviation(cf.Table3).Forexample,theaverage(indirect) (limited to just the evaporation during growth) and the
energy consumption for urea was 11.18±2.55 kJ/L with a energyrequiredtotreattherecycledwaterwouldneedtobe
standard deviation of 8.9 kJ/L. The uncertainty in this added (cf. [10, 41] for additional water intensity analysis).
measurement is the average measurement error for the On average, the direct energy inputs account for 94.2%
energy consumption by urea of the five batches. The of the total energy requirement. The indirect energy inputs,
standard deviation is high because different nutrient which include water, nutrients, CO , etc., account for 5.8%
2
feeding schedules were implemented throughout the year, ofthetotalenergyconsumed.Theenergyequivalentvalues
resulting in different nutrient consumption for each batch. of the non-energy inputs represent the total embedded
Similar variability exists for many inputs. energy for their production, and are therefore much greater
Onaverage,the energyconsumedforgrowth,harvesting, than the chemical energy content of each input. For
celllysing,andlipidseparationsaccountfor96.23%,0.89%, example, the embedded energy content of CO (g ,
2 CO2
0.15%,and2.73%,ofthetotalrequirement,respectively.The whichresultsfromcollectionandcompression)isestimated
energyrequirementsaredominatedbygrowthinputs,andof at 7.33 MJ/kg [12, 19]. The most significant non-energy
these inputs, mixing, lighting, air compression, and CO inputsareCO andheptane,whichaccountedfor2.7%and
2 2
consumption represent the parameters with the most signif- 1.6% of the total energy consumption on average, respec-
icant contributions, as shown in Fig. 4. Mixing in the pond tively.Approximately36kgofCO wereconsumedperkg
2