Bioresource Technology 151 (2014) 19–27

Contents lists available at ScienceDirect

Bioresource Technology journal homepage: www.elsevier.com/locate/biortech

Evaluating microalgal integrated biorefinery schemes: Empirical controlled growth studies and life cycle assessment Lindsay Soh a, Mahdokht Montazeri b, Berat Z. Haznedaroglu c, Cuchulain Kelly a, Jordan Peccia a, Matthew J. Eckelman b, Julie B. Zimmerman a,d,⇑ a

Department of Chemical and Environmental Engineering, Yale University, United States Department of Civil and Environmental Engineering, Northeastern University, United States Department of Civil, Structural and Environmental Engineering, University at Buffalo, United States d School of Forestry and Environmental Studies, Yale University, United States b c

h i g h l i g h t s  Marine and freshwater microalgae

were comparatively grown varying media nitrate.  Lipid, starch, and protein contents were quantified.  LCA assessed energy consumption, GHG emissions, and eutrophication potential.  Non-lipid fraction composition presented significant trade-offs among endpoints.  Tailoring species/growth conditions can enable a sustainable algal biorefinery.

a r t i c l e

i n f o

Article history: Received 12 July 2013 Received in revised form 2 October 2013 Accepted 4 October 2013 Available online 18 October 2013 Keywords: Algae Integrated biorefinery Biodiesel Life cycle assessment

g r a p h i c a l a b s t r a c t indirect processes electricity generation nutrient production water delivery bioreactor materials

CO 2 delivery/ air strip

direct processes CO 2 electricity water nutrients

electricity flocculants

Cultivation

Harvesting water

solvent production harvesting technology heat production

N,P electricity heat solvent

methanol production

Non-lipid Processing

biomass waste

transport

catalyst production

energy energy

Lipid Extraction

electricity heat methanol catalyst

Biodiesel Conversion

glycerol Algal Biodiesel

a b s t r a c t Two freshwater and two marine microalgae species were grown under nitrogen replete and deplete conditions evaluating the impact on total biomass yield and biomolecular fractions (i.e. starch, protein, and lipid). A life cycle assessment was performed to evaluate varying species/growth conditions considering each biomass fraction and final product substitution based on energy consumption, greenhouse gas emissions (GHG), and eutrophication potential. Lipid for biodiesel was assumed as the primary product. Protein and carbohydrate fractions were processed as co-products. Composition of the non-lipid fraction presented significant trade-offs among biogas production, animal feed substitution, nutrient recycling, and carbon sequestration. Maximizing total lipid productivity rather than lipid content yielded the least GHG emissions. A marine, N-deplete case with relatively low lipid productivity but effective nutrient recycling had the lowest eutrophication impacts. Tailoring algal species/growth conditions to optimize the mix of biomolecular fractions matched to desired products and co-products can enable a sustainable integrated microalgal biorefinery. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction As renewable energy sources increase in their prevalence and use, the research and adoption of efficient processes and technolo⇑ Corresponding author. Address: 9 Hillhouse Avenue, Room 313B, New Haven, CT 06520, United States. Tel.: +1 202 432 9703. E-mail address: [email protected] (J.B. Zimmerman). 0960-8524/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.biortech.2013.10.012

gies is vital for the field to sustainably expand. Microalgae have been indicated as a robust potential alternative to traditional fuel resources due to their ability to be used as a feedstock for a variety of biofuels and other value-added chemicals (Pienkos and Darzins, 2009). Microalgal biomass production offers a number of advantages over conventional biomass production, including higher photosynthetic productivity, use of otherwise nonproductive land, reuse and recovery of waste nutrients, use of saline or brackish waters,

20

L. Soh et al. / Bioresource Technology 151 (2014) 19–27

and reuse of CO2 from power plant flue gas or similar sources (Pienkos and Darzins, 2009). While algal biofuels are promising, particularly for the production of biodiesel, current practices and technologies are not sufficient to make large-scale production energetically or economically favorable with liquid fuel as the sole salable product. Thus, improvements and innovations to the biofuel production process must be achieved including the development of biorefinery approaches to recover energy and nutrients as well as accommodate the non-lipid fractions (i.e. carbohydrate, protein) of algal biomass. Currently, there is a significant focus on growing microalgae specifically for biofuel applications. Possible fuel products include biocrude, biogas, biohydrogen, bioethanol, and biodiesel (Brennan and Owende, 2010), each of which have advantages and disadvantages due to feedstock processing and limitations. The feedstock requirements for these processes can vary significantly, and optimization of the microalgal biomass will differ based on the process and target fuel selected. For example, bioethanol production is optimal with a high starch (carbohydrate) feedstock, while biodiesel is produced from triglycerides found in the lipids (Mata et al., 2010). Biocrude oil and biogas can be produced through thermochemical conversion processes where the maximization of total biomass is ideal (Brown et al., 2010), yet these processes do not allow for the harvesting of other valuable co-products such as protein for animal feed or nutraceuticals and reduce the potential for nutrient recycling (Spolaore et al., 2006). In fact, business models have shown that algae cultivated for biofuel alone will yield comparatively low profits or returns since (1) biofuel is a relatively low value commodity and (2) only a fraction of algal biomass can be utilized for biofuel leaving a significant percentage of ‘‘waste’’ if not managed for further value recovery (Subhadra and Edwards, 2011). Thus, it is economically and environmentally critical to expand the downstream processing of biomass to other finished products besides fuels in a biorefinery setting (Stephens et al., 2010). This multiproduct paradigm aligns with the model used by crude oil refineries where multiple value-added fuels and chemicals are produced. This type of approach has been explored for a biorefinery in a life cycle assessment (LCA) of switchgrass by (Cherubini and Jungmeier, 2010) where it was found that significant GHG and fossil energy savings could be achieved when compared to a fossil reference system, although there are potentially larger eutrophication and acidification impacts. The study did not compare the impacts of a fuel only versus a biorefinery model, which will be important in demonstrating the benefit of a biorefinery configuration versus a singular focus on an individual end product. In another study, a co-product market analysis and water footprint, not considering energy or GHGs, was conducted for an algal biorefinery (Subhadra and Edwards, 2011) demonstrating clear advantages for a multiproduct paradigm to attain high operational profits. In order to successfully implement the biorefinery model, technological innovation as well as gains in efficiency must be made. These efficiencies may be realized through cultivation of the appropriate strain and optimization of the growth conditions for the intended end products. There are wide ranges observed for lipid, protein, and carbohydrate composition depending on algal species as well as the growth conditions (Griffiths and Harrison, 2009). For example, growing microalgae in N-deplete conditions promotes cellular lipid accumulation in many species (Griffiths and Harrison, 2009). Attempts to exploit this high lipid content for the production of biodiesel while simultaneously reducing nutrient costs, however, is challenged by a low total biomass growth in microalgal cultures (Rodolfi et al., 2009). This trade-off presents a challenge towards optimization of strain and nutrient loadings for the appropriate mix of desired outputs (i.e. total

biomass, high lipid, protein, or starch content) while minimizing resource inputs and environmental impacts. Previous LCA studies have evaluated the embedded energy, water use, and environmental impacts associated with many aspects of the microalgae biofuel production process including coproduct production (Brennan and Owende, 2010; Brentner et al., 2011; Campbell et al., 2011; Clarens et al., 2010; Jorquera et al., 2010; Lardon et al., 2009; Shirvani et al., 2011; Sills et al., 2012; Subhadra and Edwards, 2011). These studies generally concluded that, although microalgae are a promising fuel feedstock, system improvements are necessary for them to become economically viable and sustainable. One crucial finding has been that effective utilization of non-fuel co-products is essential for the overall system to achieve a positive energy return on investment (EROI) (Sills et al., 2012). Differences in EROI ratios from previously published studies are a result of inconsistencies in functional units, system scope, boundaries, key parameters, and other assumptions (Liu et al., 2012; Sills et al., 2012). Further, many of these studies assume non-specific or freshwater algal species. Of the few studies that considered marine species, (Campbell et al., 2011) described a coastal algae production system based on pumped seawater and assumes similar lipid production and profiles to freshwater species, which is likely unrealistic based on Griffiths and Harrison (2009) and the findings reported below. (Jorquera et al., 2010) assessed different reactors for marine algal growth and obtained positive net energy ratios (NERs) for oil production in both closed reactors and open ponds; however, downstream processing of the lipid and oilcake was beyond the scope of their study. Finally, Yang et al. (2011) considered life cycle water and nutrient reductions associated with seawater rather than freshwater for cultivation but used reported growth results only for Chlorella vulgaris (a freshwater strain) at N-replete conditions. The work presented here encompasses controlled growth studies for multiple freshwater and marine species where the biomass composition is well characterized for not only lipid production but also starch and protein contents across different growth conditions as not previously seen in the literature. Previous LCAs have also considered the trade-off between high and low nitrogen growth conditions (Campbell et al., 2011; Lardon et al., 2009). In an LCA of C. vulgaris, significant differences were reported in cumulative energy demand for N-replete and N-deplete conditions (Lardon et al., 2009). It was found that N-deplete conditions yield more lipids for less cumulative energy demand, in part as a result of reduced fertilizer input. However, this reduction in life cycle energy requirements was offset by a decrease of 55–65% in embodied energy remaining in the oilcake after lipid extraction. In a setup where the oilcake is combusted or anaerobically digested for biogas, this decreased energy content reduces on-site heat and power meaning that external fuel sources must instead be used. This result is important because fertilizer inputs have been shown to significantly impact the overall energy and GHG balance of algal fuels (Clarens et al., 2010) while much of the research on nutrient-limited conditions has focused on the biomolecular composition and productivity of the lipid fraction only (Campbell et al., 2011; Lardon et al., 2009). In general, previous LCA studies have focused on a single production scheme (typically lipid for biodiesel or starch for bioethanol), rather than considering trade-offs among each fraction of algal biomass for production of multiple salable co-products. Many researchers have used common assumptions about various algae strains, including lipid content, volumetric productivity, and nutrient inputs, based on stoichiometric requirements and ideal conditions rather than empirical data (Clarens et al., 2010; Liu et al., 2012; Shirvani et al., 2011; Subhadra and Edwards, 2011). In this work, we use experimental results from controlled growth studies (nitrogen replete and deplete) to provide LCA data

L. Soh et al. / Bioresource Technology 151 (2014) 19–27

for four different microalgae strains – two freshwater and two marine species – while considering material, energy, and media inputs. Variation in nutrient inputs specifically occur between replete and deplete conditions and between source water types. The resulting biomolecular composition (lipid, starch, protein) and biomass productivity values are used to quantify the life cycle environmental impacts from the algal biorefinery for three key critical environmental midpoints: cumulative energy demand, GHG emissions, and eutrophication potential. This LCA considers lipid-based biodiesel as the primary product and carbohydratebased bioelectricity and protein-substituted animal feed as coproducts within the biorefinery. In this way, targeted cultivation and species selection can be evaluated to inform potential microalgal integrated biorefinery schemes. 2. Methods 2.1. Chemicals and materials All chemicals for media growth were supplied by either Sigma– Aldrich or J.T. Baker and were of reagent grade quality. Seawater was harvested from Long Island Sound, filtered, and pasteurized as described in (UTEX, 2011). Solvents chloroform, acetone, ethanol, and methanol were supplied by J.T. Baker. CHROMASOLVÒ heptane and LC–MS CHROMASOLVÒ 2-propanol were supplied by Sigma–Aldrich and Fluka, respectively, for chromatographic analysis. 2.2. Algal growth experiments Algae were cultivated in triplicate 1 L Erlenmeyer flasks filled with 500 mL of culture media and supplied with 0.75 L/min air enriched with 2% carbon dioxide bubbled into the reactors. Algae strains were purchased from the Culture Collection of Algae at the University of Texas at Austin (UTEX, 2011) and grown in the specified media. Freshwater strains – Neochloris oleoabundans (Chantanachat and Bold 1962, UTEX #1185) and Chlorella sorokiniana (Shihira and Krauss 1965, UTEX #260) – were grown in Bold 3N Medium (Bold, 1970) and marine strains – Nannochloropsis oculata ((Droop) Hibberd 1981, UTEX #LB 2164) and Tetraselmis suecica ((Kylin) Butcher, UTEX #LB 2286) – in Enriched Seawater Medium (Bold and Wynne, 1978) without the specified nitrogen content which was modified according to the experimental protocol for N-replete and N-deplete conditions. The nitrogen concentration of the background media was taken into account, and potassium nitrate was used to bring the total nitrogen concentration up to 10 mg/L (N-deprived) and 100 mg/L (N-replete) – as N. The microalgae were supplied with 14 h light and were constantly mixed with a magnetic stir bar. In order to account for the volume of reactor contents harvested for analyses, a reactor for each N and N+ condition was simultaneously cultivated and used to replenish the harvested volume to maintain a constant reactor volume. 2.3. Algal sampling and harvesting Optical density measurements at 610 nm of the cell cultures were taken daily and correlations with cell dry mass and cell number concentration estimated by via calibration curves. For these calibration curves, cell mass per unit volume was measured for lyophilized cells. Cell number per volume was measured by counting under microscope with a hemocytometer. For all analyses, cells were harvested in late exponential growth phase as determined by previously established growth curves, which correlates to 8 or 9 days of growth depending on the species. During harvesting a

21

fixed volume of culture was transferred into falcon tubes, centrifuged for 5 min at 12,000 rpm and 4 °C, decanted, and transferred to microcentrifuge tubes in which samples were frozen at 20 °C until extraction and further analyses. 2.4. Extraction and analyses All analyses were run in duplicate on each of the three replicates. Lipid: Lipid extraction, transesterification, and fatty acid methyl ester analysis was performed as detailed by the conventional solvent extraction method in (Soh and Zimmerman, 2011). Glyceryl nonadecanoate was used as an internal standard by addition to a subset of each strains’ samples and was used to calculate the extraction efficiency for each strain per cell mass. Protein: Thorough method development was performed to insure maximal protein extraction and replicable analyses varying extraction solutions, homogenization timings, number of extractions and background analyses. A solution of 0.1 M NaOH and 0.25 mL/L Tween 20 as well as 0.07 g of 0.5 mm and 0.2 g of 0.1 mm ceramic beads were added to the pelleted cells and homogenized on a bead beater for 1 min similar to (Meijer and Wijffels, 1998). The samples were then centrifuged for 1 min at 7000 rpm and the supernatant collected. For complete extraction, this process was repeated two more times, and the combined supernatants were then analyzed for protein using the PierceÒ BCA Protein Assay Kit (Thermo Scientific), following the manufacturer’s instructions. Analysis was performed in 96-well microplates using the provided bovine serum albumin standard to make a calibration curve on each plate. In order to mitigate any interference with pigments, each sample was also run with a sample blank and the background absorbance subtracted before calculation of protein concentration. Starch: Similar method development procedures were followed as for protein extraction. In the end pelleted cells were pre-extracted with acetone and ethanol in order to remove interfering substances as in (Fernandes et al., 2012). Acetone was added to the algae, and the samples were homogenized for 1 min on a bead beater. The samples were then centrifuged at 14,000 rpm for 1 min and the acetone was discarded. This step was repeated with ethanol until no further visible pigments were extracted from the cells. After preextraction, the cells were then completely transferred to glass test tubes for starch extraction. The tubes were centrifuged at 4000 rpm for 1 min and the supernatant discarded. Analysis was performed using a starch assay kit (Sigma SA 20) following manufacturer’s instructions for starch extraction with DMSO and HCl with all analyses scaled down to the appropriate volume. Nitrate: Nitrate concentrations were measured from the filtered supernatant of centrifuged cells using a nitrate test kit (Nitrate Elimination Company, Inc.). Analysis was performed as per the manufacturer’s instructions for both freshwater and seawater species in 96-well microplates. 2.5. Life cycle assessment A life cycle assessment was performed comparing the various production schemes: freshwater and seawater species under both nitrogen replete and deprived conditions, with downstream processing of each biomass fraction into a target product: lipid to biodiesel, starch to bioelectricity, and protein to animal feed (Fig. 1). Material and energy requirements for each scheme were estimated using the Algae Process Description (APD) module of the GREET 2012 rev2 model (Frank et al., 2011) with minor modifications as follows. GREET assumes internal recycling of water and nutrients from dewatering and anaerobic digestion (AD) back to cultivation. Here, GREET-specified water quantities and pumping requirements were preserved, while nutrient inputs were altered according to the media recipes used in the experimental set-up. Nutrient

22

L. Soh et al. / Bioresource Technology 151 (2014) 19–27

(A) Cultivation water

x1 Harvesting

Protein Extraction

a1 solvent

x3

a2

Lipid Fraction a1=1.04 kg bio-oil

(B)

Protein Fraction

x4 Digestion a5

a3

Methane

x5 Residue Management

Cultivation

x1

Harvesting

x2=0.9*x1

Lipid Extraction

x3=x2-a1

Protein Extraction x4=x3-a2

x2 Lipid Extraction

Flow from Mass Balance Algae Process Equation

a4

Fertilizer (50% of C and P; 24% of N in x5 flow)

Digestion

x5=x4-a3

Residue Mgmt

x5=a4+a5

Flow Neo+ Neo- Chl+ Chl- Tet+ Tet- Nan+ Nanx1

13.3

3.5

6.5

4.6

72.4

9.1

6.9

5.6

x2

12.6

3.3

6.2

4.4

68.8

8.6

6.5

5.3

x3

15.0

1.8

3.7

2.4

73.5

9.2

4.3

4.1

x4

12.3

1.4

3.2

2.1

38.8

5.5

2.8

3.7

x5

6.2

1.3

2.8

1.9

35.7

4.1

3.0

2.3

a1

1.04

1.04

1.04

1.04

1.04

1.04

1.04

1.04

a2

2.7

0.4

0.6

0.3

34.8

3.6

1.4

0.3

a3

2.4

0.2

0.5

0.3

7.7

0.9

0.6

0.8

a4

0.3

0.1

0.1

0.1

1.3

0.2

0.1

0.1

a5

5.9

1.2

2.7

1.8

34.4

3.9

2.8

2.2

Fig. 1. Mass flows through life cycle stages included in the scope of the study as described by (A) and detailed for each growth scenario (species/N-loading) in (B) where for N. oleoabundans (Neo), C. sorokiniana (Chl), N. oculata (Nan), and T. suecica (Tet) under N-deprived ( ) and N-replete (+) growth conditions.

recipes for each media type (in g/L) were modeled using primarily unit processes from the ecoinvent 2.2 LCI database and adjusted for background levels of N in freshwater and seawater. Where data did not exist for specific nutrients, new unit processes were created using current primary industrial production routes and stoichiometric equivalents. Unit process proxies from the ecoinvent database were used in the remaining few cases. (Further details on modeling nutrient inputs can be found in the Supplementary Information). The APD module assumes as a reference flow 1 kg of bio-oil, with protein and carbohydrate fractions as co-products. Subsequent transesterification to biodiesel was modeled, including chemical and energy inputs, was modeled using the main GREET model. Following the algal biorefinery model suggested in (Brune et al., 2009), lipid extraction is followed by protein extraction for animal feed, with digestion of the remaining starch and residues for electricity, heat, nutrient, and sludge co-products. Internal nutrient flows for N and P followed GREET assumptions, including 5% N volatilization for open ponds, 95% re-utilization of N and P in AD supernatant, displacement of N and P fertilizers, and C sequestration in soils by AD solids. Nutrient flows were adjusted for each species and growth regime as were model parameters for the protein–starch–lipid fractions determined experimentally. These fractions also affect the production of methane in the AD by changing the relative inputs of C and N to the unit, which was modeled in GREET following the biogas model of (Sialve et al., 2009) with recommended adjustments for the digestible fraction (Frank et al., 2011). The baseline harmonized Algae Process Description model assumes an open pond reactor system due to the high degree of variability in reported energy requirements for mass transfer in photobioreactors; however, an air-lift tubular reactor is also specified in the model with zero mixing energy. In order to preserve comparability with reported results, open ponds were modeled here, though the empirical growth studies relied on bench-scale closed reactors with openings for air exchange. GREET model outputs for energy and chemical use were matched with ecoinvent LCI data, as detailed in the SI. Life cycle impact assessment was carried out for three specific environmental impact categories: cumulative energy demand (CED 1.08), greenhouse gas emissions (IPCC 2007

GWP100), and eutrophication (using the TRACI 2 LCIA method). These endpoints were chosen to consider trade-offs associated with nutrient use, lipid productivity, and co-product generation using freshwater and marine species. 3. Results and discussion 3.1. Algal growth and composition The four algal strains were chosen to represent both freshwater (N. oleoabundans and C. sorokiniana) and marine (N. oculata and T. suecica) species, which have all been previously studied for their use as biofuel feedstock (Mata et al., 2010). As indicated in Table 1, the culture density and mass yields of biomass for the N-replete conditions were much higher than the N-deprived set as expected (Griffiths et al., 2012). For all N-deprived conditions nitrate concentrations were near or below the method detection limit confirming that the availability of nitrate is a limiting factor for cell growth in this system. For the N-replete set (starting at 100 mg/L as N), the freshwater species’ nitrate supply is significantly depleted though complete nitrogen starvation is not yet reached. The marine species still show a significant portion of nitrate left in the media despite nearing the end of their exponential growth implicating the limitation of another key nutrient, light, or aeration. 3.2. Fatty acid methyl ester content and composition The fatty acid methyl esters (FAME) that could be produced from extracted lipids from each algal species was quantified. The derived FAME content per cell mass and productivity was determined for each algal species and fell within typical ranges (Griffiths et al., 2012). In all cases nitrogen limitation led to higher FAME content per dry algae mass (between 8% and 75% higher) but the FAME productivity per volume was often much lower due to the significantly inhibited biomass growth (Fig. 2). This feature is most evident with C. sorokiniana in N-deplete growth conditions where the high lipid content per cell (35%, mg FAME produced/mg cell mass) does not compensate for the low total biomass growth in

23

L. Soh et al. / Bioresource Technology 151 (2014) 19–27

Table 1 Conditions of algal cultures at harvest on day 8/9 during exponential growth phase for four species (two freshwater and two marine) in nitrogen deplete and replete conditions. Uncertainty values represent standard error between triplicates. Algae strain Freshwater

Neochloris oleoabundans (Neo)

Nitrogen

Cell density (cells/mL  106)

Mass density (g/L)

Nitrate concentration (mg/L as N)

Deplete ( ) Replete (+)

39.9 ± 7.5 104.7 ± 11.9 9.26 ± 0.38 69.4 ± 13.7

0.69 ± 0.13 1.83 ± 0.21 0.28 ± 0.01 2.18 ± 0.43

b.d.l.a 11.3 ± 6.3 0.16 ± 0.01 14.9 ± 8.1

1.00 ± 0.21 5.59 ± 0.81 60.5 ± 5.7 188.6 ± 9.8

0.30 ± 0.08 1.54 ± 0.25 0.56 ± 0.05 1.79 ± 0.09

b.d.l.⁄ 61.3 ± 2.3 b.d.l.⁄ 67.6 ± 9.9

Chlorella sorokiniana (Chl) + Marine

Tetraselmis suecica (Tet) + Nannochloropis oculata (Nan) +

b.d.l. = Below detection limit.

FAME produced/ culture volume (mg/L)

a

700 600

Neo

Chl

Tet

Nan

500 400 300 200 100 0 0

10

20

30

40

50

FAME produced/cell mass (mg/mg, %) Fig. 2. FAME content and productivity of algal species, N. oleoabundans (Neo), C. sorokiniana (Chl), N. oculata (Nan), and T. suecica (Tet), with N-replete (solid symbols) and N-deprived (outlined symbols) growth conditions. Error bars represent standard error between experimental replicates.

terms of total lipid production per volume (90 mg FAME/L cell culture). It is interesting to note that C. sorokiniana grown in N-replete conditions has the highest FAME productivity per unit volume (580 mg/L) due to the high biomass yield even though the FAME content is moderate. N-deprived N. oleoabundans had the highest FAME content of 42% and in this instance yielded higher lipid productivity per volume (350 mg/L) than the N-replete condition (220 mg/L). A similar trend is observed for one of the marine species, T. suecica (80 mg/L for N vs. 50 mg/L in N+). Though not yielding the highest lipid productivity on a per volume basis, the observed enhanced FAME productivity per volume given the lower nutrient requirements will present an interesting tradeoff in terms of resource use and environmental impact to be quantified by the LCA. The composition of FAME produced via transesterification of the lipid extract varies significantly between each species and in some instances with nitrate loading (Fig. 3). The variation in FAME profile between species can potentially be used as a means to control the biodiesel and co-product characteristics; that is, specific strains and certain growth conditions may be chosen for certain desired end products. The FAME profile is further quantified in terms of FAME properties including average chain length, percentage polyunsaturated fatty acids (>1 double bond), the average degree of unsaturation, and the percentage of unsaturated fatty acids in Table 2 for each species under N-replete and N-deplete conditions. These metrics for other commonly used biodiesel feedstocks are also listed for comparison. FAME profiles and characteristics can inform the type of products that would be preferable; for instance, biodiesel properties

Fig. 3. Fatty acid methyl ester profile of lipid extracts for N. oleoabundans (Neo), C. sorokiniana (Chl), N. oculata (Nan), and T. suecica (Tet) under N-deprived (N ) and N-replete (N+) growth conditions.

such as oxidative stability and cold flow are extremely important in defining biodiesel use (Griffiths et al., 2012; Moser and Vaughn, 2012). In general the FAME from algae are shorter than that of canola, palm, soy, and sunflower oils with an average chain length across all species and growth conditions of 16.8 compared to 17.9, 17.1, 17.8, and 18.0 for the oils respectively. These shorter chain lengths may be beneficial for cold flow properties and viscosity (Knothe, 2005; Moser and Vaughn, 2012), but are still long enough to not significantly affect the heat of combustion and cetane number (Knothe, 2005; Moser and Vaughn, 2012). In fact, the four algal strains have almost non-existent amounts (61.6% of total FAME production) of very long chain fatty acids (P20 carbons), which have significant effect on fuel viability; if found in high concentrations, these long chain FAME will cause the fuel product to suffer in terms of kinematic viscosity, derived cetane number, and cold flow properties (Moser and Vaughn, 2012). The percentage of polyunsaturated fatty acids (%PUFA) ranges from 9.2% to 50.7%, which is within the range of the conventional biomass feedstocks (8.2–61.3%) except for the two freshwater strains in N-replete conditions (68% PUFA). Minimizing %PUFA is necessary to ensure oxidative stability of the biodiesel product (Moser and Vaughn, 2012). Further, the unsaturated lipid percentages (48.2–76.9%) fall in the range of the other feedstocks (51.8–92.3%) except for T. suecica with 22.0% due to large amounts of methyl palmitate (C16:0) and methyl stearate (C18:0). The percent of unsaturated FAME needs to be high enough to favor cold flow while polyunsaturated FAME need to be moderated as they have poor oxidative stability (Moser and Vaughn, 2012). These results suggest that the effects of strain and growth conditions can play an important role in the properties of the final fuel

24

L. Soh et al. / Bioresource Technology 151 (2014) 19–27

Table 2 Lipid profiles of N. oleoabundans (Neo), C. sorokiniana (Chl), N. oculata (Nan), and T. suecica (Tet) grown under nitrogen replete and deplete conditions. The lipid profiles of other established biofuel feedstocks from (Moser, 2008) are included for comparison. Biomass feedstock Freshwater algae

Neochloris oleoabundans (Neo)

Nitrogen

Average chain length

% Polyunsaturated fatty acid

% Unsaturated FAME

Deplete ( ) Replete (+)

17.24 ± 0.09 16.38 ± 0.15 17.16 ± 0.02 17.12 ± 0.04

50.73 ± 2.52 67.92 ± 3.91 47.50 ± 3.16 68.37 ± 1.56

62.14 ± 5.50 71.63 ± 7.55 56.14 ± 5.57 76.91 ± 2.62

17.06 ± 0.05 17.16 ± 0.15 16.40 ± 0.03 16.21 ± 0.03

11.49 ± 2.82 9.15 ± 4.65 11.87 ± 0.47 12.72 ± 0.42

48.21 ± 8.06 21.96 ± 13.28 59.85 ± 2.74 54.68 ± 4.31

17.93 17.10 17.79 17.96

27.8 10.4 61.3 8.2

Chlorella sorokiniana (Chl) + Marine algae

Tetraselmis suecica (Tet) + Nannochloropis oculata (Nan) +

Crop-based

Canola Palm Soy Sunflower

N/A N/A N/A N/A

product and must be chosen carefully to allow for an efficient and effective biodiesel production process. 3.3. Biochemical compositions: lipid, protein, starch As seen in Fig. 4, the lipid, protein, and starch compositions vary significantly between species and less so between nitrogen conditions. For instance, T. suecica, while lower in lipid (11.6% FAME for N , 1.6% for N+) than the other species, is high in protein (41.9% protein for N , 49.3% for N+ compared to an average of 14.4% for the other species) and thus may be considered for purposes other than fuel such as animal feed (Spolaore et al., 2006). As expected due to the necessity of nitrogen for amino acid synthesis, the protein content of the N-deficient algae is lower than the N-replete. When considering the appropriateness of microalgae for a given application, this composition must be weighed to find the most economical and environmentally preferable strain and product pair. For instance, the high biomass density that is attained by the freshwater species may be ideal for thermochemical conversion into biocrude (Brennan and Owende, 2010), though the high nutrient inputs and challenges in loss of ability to isolate coproducts may unfavorably tip the economic, energy, and resource balance. Alternatively, the species with high starch content may be more suitable for anaerobic digestion or biofermentation and subsequent production of bioethanol (Mata et al., 2010). From these controlled growth studies and subsequent analyses, it is clear that there are different opportunities associated with

Fig. 4. Lipid, protein, and starch profiles (as percent dry mass) of N. oleoabundans (Neo), C. sorokiniana (Chl), T. suecica (Tet), and N. oculata (Nan).

92.3 51.8 85.4 90.2

different algal compositions, productivities, growth conditions, and product endpoints. The following life cycle assessment was done in order to compare the potential environmental benefits and impacts associated with an algal biorefinery considering different compositions of the biomass feedstock. It is important to consider that the data reflected here represent results from bench-scale growth studies where the productivities are not necessarily representative of what may be obtained at large scale. Depending on several growth parameters including reactor size, configuration, and orientation, productivities may vary significantly, though it has been found that optimization of these parameters may in fact preserve high productivities when growing in large volumes (Ugwu et al., 2008). Scale-up of these processes remains a major challenge in terms of bio-process engineering for algal growth systems. However, the fast pace of reactor development has been accompanied by an increase of biomass productivities in large-scale reactors, which are starting to approach those observed on a smaller scale. The following LCA study based on empirical data is helpful to provide a fundamental basis for the analysis and subsequent results.

3.4. Life cycle assessment: energy consumption, greenhouse gas emissions, and eutrophication potential The controlled growth studies show clear differences between the algae species considered; these differences are also reflected in the LCA results for energy consumption, GHG emissions and eutrophication impacts. Fig. 5 shows LCA results for each growth scenario, with contributions from each life cycle stage to the right of the y-axis and avoided burdens (or environmental benefits) due to co-products, to the left of the y-axis. Several patterns are evident. First, it appears that while all of the growth scenarios produce biodiesel with positive net GHG emissions, the N-replete freshwater scenarios Chl+ and Neo+ have among the lowest at 2.4 and 0.5 kg CO2e per kg of biodiesel, respectively. These cases had lipid contents significantly lower than their N-deplete counterparts but total volumetric lipid productivities were among the highest of all scenarios with Chl+ leading at 550 mg/L. The Neo+ result is equivalent to 13 g CO2e per MJ of fuel, well under the 50% reduction threshold set by the RFS Baseline Renewable Fuel Standard (RFS) as compared to the RFS Baseline for petroleum diesel (U.S. Environmental Protection Agency, 2010). The GHG results underlines the fact that using nutrient deprivation to enrich a particular biomass fraction while sacrificing total lipid productivity may not be desirable when considering the entire system. In all cases but one (Nan ), production of nutrients was the largest contributor to GHG emissions, ranging from 27% for Tet+

25

L. Soh et al. / Bioresource Technology 151 (2014) 19–27

dewatering

electricity

fertilizer + C storage

conversion

Avoided Impacts

Impacts

protein meal

% Lipids

natural gas

recovery

extraction

cultivation

22%

Nan (N-)

18%

Nan (N+)

Growth Scheme

13%

Tet (N-)

2%

-690

Tet (N+)

26%

Chl (N-) Chl (N+)

19%

Neo (N-)

35%

Neo (N+)

9%

-200

-150

1180

-100

-50

0

50

100

150

200

250

Primary Energy Use (MJ)

Nan (N

-)

Nan (N+) Tet (N-) Tet (N+)

-55

113

Chl (N-) Chl (N+) Neo (N-) Neo (N+)

-15

-10

-5

0

5

10

15

20

GHG Emissions (kg CO2e)

Nan (N-) Nan (N+) Tet (N-) Tet (N+) Chl (N-) Chl (N+) Neo (N-) Neo (N+)

-0.10

0.00

0.10

0.20

0.30

Eutrophication (kg N) Fig. 5. Life cycle impacts for GHG emissions, eutrophication, and primary energy use per kg of biodiesel for N-replete and N-deplete growth conditions.

to 64% for Chl , even though the GREET model includes internal recycling of N and P after biogas digestion. The exception is for the marine species Nan , where N-deplete conditions and a fairly high nutrient recycling rate of >60% drive down impacts of nutrient production, leaving electricity use for mixing and CO2/water delivery as the largest contributor to GHG emissions. Co-product credits for GHG emissions and primary energy use in all cases is due to surplus electricity generation from biogas production through anaerobic digestion, whereas substitution of algal protein for soybean meal is the largest credit to eutrophication impacts.

Considering primary energy use, energy consumption exceeds energy delivered in biodiesel (assuming biodiesel HHV of 37 MJ/kg) leading to EROI 100% of the heat and a substantial portion of the total electricity requirements for cultivation up to lipid transesterification. If a different biorefinery setup were used that processed the starch fraction into bioethanol rather than employing anaerobic digestion, on-site biogas production would likely be reduced, potentially requiring external energy sources to make up the lost heat and electricity (Further integration is also possible, for example by utilizing surplus heat from other industrial processes located with or near the biorefinery). It was hypothesized that the existence of nutrients and micronutrients in seawater would lower the impacts associated with growth media for marine algal species, as fewer synthetic chemicals would be required. Background concentrations of nitrogen in the feed water and buffers contributed toward total nitrogen specified in the growth media. These levels were insignificant in the Nreplete cases, but comprised 17% (marine) and 42% (freshwater) of total N (without recycling) in the N-deplete cases. The concomitant reductions in required NaNO3 inputs therefore had relatively small benefits relative to total impacts in the N-replete cases, while for the N-deplete cases, life cycle impacts were driven by the production of chemicals other than NaNO3, and these differed between marine and freshwater media. In particular, the eutrophication impacts seen in the freshwater cases, C. sorokiniana and N. oleoabundans, (Fig. 5) are largely driven by the production of mono- and dipotassium phosphate, which has much higher life cycle eutrophication impacts than the sodium glycerophosphate required for the marine media.

in the N-replete conditions, and other nutrients were likely limiting growth such as iron for marine microalgal species. Optimization of the media will likely increase the biomass density and decrease overall life cycle impacts. Other implications involved with cultivation in untreated seawater must also be considered, such as the presence of contaminating biota which could necessitate a resilient algal strain or energy intensive seawater pretreatment such as filtering or pasteurization as recommended by UTEX (2011).

3.5. Implications for microalgal integrated biorefinery schemes

Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.biortech. 2013.10.012.

The orientation of the GREET model and most LCAs of algal biomass is toward biodiesel or green diesel; however, algal biorefineries need not be optimized for lipid productivity. For example, the growth scenario with the lowest lipid productivity (only 2% for Tet ) clearly had the highest impacts for both GHG emissions and eutrophication, due to the large quantities of this microalgae required to produce a unit of biodiesel. Instead of biodiesel, this species may be well suited to fermentation into ethanol or direct use as an animal feed supplement due to its large starch and protein content (Fig. 4). Consequently, avoiding the significant energy and chemical resource consumption associated with lipid extraction and conversion. While this study has emphasized microalgae cultivation, it is important to consider other life cycle aspects of this comparison between freshwater and marine species and N-replete and N-deplete cases. Several other LCA studies (Campbell et al., 2011; Jorquera et al., 2010) that have modeled coastal production with marine species have assumed various combinations of fertilizers for the growth media, with most omitting micronutrients under the assumption that these are already present in non-limiting quantities in seawater. If micronutrient sources are indeed flexible, then recipes for growth media may be optimized using a strategy of minimizing high-impact synthetic chemicals. In this study, the growth of the marine species did not appear to be nitrate limited

4. Conclusions Maximizing productivity of a single algae fraction does not a priori lead to optimal environmental outcomes. Microalgae with higher lipid productivity do not necessarily lead to lower environmental impacts. However, engineered increases in lipid productivity should be carefully balanced against intended uses of the nonlipid fractions, particularly given the significant benefits realized through anaerobic digestion of the starch fraction. Targeted extraction of high-value compounds for pharmaceutical or chemical industries may greatly improve the economic performance of algal production systems, while beneficial use of remaining fractions for lower-value end-uses can improve overall biorefinery performance in environmental terms. Acknowledgements The authors would like to thank Joshua Curry for his help with protein extraction and analysis. This article was developed under STAR Fellowship Assistance Agreement no. FP-91717301-0 awarded by the U.S. Environmental Protection Agency (EPA). It has not been formally reviewed by EPA. The views expressed in this publication are solely those of the authors and EPA does not endorse any products or commercial services mentioned in this publication. Appendix A. Supplementary data

References Bold, H., Wynne, M., 1978. Introduction to the Algae: Structure and Reproduction. Prentice Hall, Princeton, NJ. Bold, H.C., 1970. Some aspects of the taxonomy of soil algae. Ann. N Y Acad. Sci. 175, 601–616. Brennan, L., Owende, P., 2010. Biofuels from microalgae—a review of technologies for production, processing, and extractions of biofuels and co-products. Renewable Sustainable Energy Rev. 14, 557–577. Brentner, L.B., Eckelman, M.J., Zimmerman, J.B., 2011. Combinatorial life cycle assessment to inform process design of industrial production of algal biodiesel. Environ. Sci. Technol. 45, 7060–7067. Brown, T.M., Duan, P., Savage, P.E., 2010. Hydrothermal liquefaction and gasification of Nannochloropsis sp. Energy Fuels 24, 3639–3646. Brune, D., Lundquist, T., Benemann, J., 2009. Microalgal biomass for greenhouse gas reductions: potential for replacement of fossil fuels and animal feeds. J. Environ. Eng. 135, 1136–1144. Campbell, P.K., Beer, T., Batten, D., 2011. Life cycle assessment of biodiesel production from microalgae in ponds. Bioresour. Technol. 102, 50–56. Cherubini, F., Jungmeier, G., 2010. LCA of a biorefinery concept producing bioethanol, bioenergy, and chemicals from switchgrass. Int. J. Life Cycle Assess. 15, 53–66. Clarens, A.F., Resurreccion, E.P., White, M.A., Colosi, L.M., 2010. Environmental life cycle comparison of algae to other bioenergy feedstocks. Environ. Sci. Technol. 44, 1813–1819. Davis, R., Aden, A., 2012. Renewable Diesel from Algal Lipids: An Integrated Baseline for Cost, Emissions, and Resource Potential from a Harmonized Model. Argonne National Laboratory.

L. Soh et al. / Bioresource Technology 151 (2014) 19–27 Fernandes, B., Dragone, G., Abreu, A., Geada, P., Teixeira, J., Vicente, A., 2012. Starch determination in Chlorella vulgaris—a comparison between acid and enzymatic methods. J. Appl. Phycol. 24, 1203–1208. Frank, E., Han, J., Palou-Rivera, I., Elgowainy, A., Wang, M. 2011. User manual for algae LCA with GREET: Version 0.0. ANL/ESD/11-7. Argonne National Laboratory. Available at: . Griffiths, M., Harrison, S.L., 2009. Lipid productivity as a key characteristic for choosing algal species for biodiesel production. J. Appl. Phycol. 21, 493–507. Griffiths, M., Hille, R., Harrison, S.L., 2012. Lipid productivity, settling potential and fatty acid profile of 11 microalgal species grown under nitrogen replete and limited conditions. J. Appl. Phycol. 24, 989–1001. Jorquera, O., Kiperstok, A., Sales, E.A., Embiruçu, M., Ghirardi, M.L., 2010. Comparative energy life-cycle analyses of microalgal biomass production in open ponds and photobioreactors. Bioresour. Technol. 101, 1406–1413. Knothe, G., 2005. Dependence of biodiesel fuel properties on the structure of fatty acid alkyl esters. Fuel Process. Technol. 86, 1059–1070. Lardon, L., He´lias, A., Sialve, B., Steyer, J.-P., Bernard, O., 2009. Life-cycle assessment of biodiesel production from microalgae. Environ. Sci. Technol. 43, 6475–6481. Liu, X., Clarens, A.F., Colosi, L.M., 2012. Algae biodiesel has potential despite inconclusive results to date. Bioresour. Technol. 104, 803–806. Mata, T.M., Martins, A.A., Caetano, N.S., 2010. Microalgae for biodiesel production and other applications: a review. Renewable Sustainable Energy Rev. 14, 217– 232. Meijer, E.A., Wijffels, R.H., 1998. Development of a fast, reproducible and effective method for the extraction and quantification of proteins of micro-algae. Biotechnol. Tech. 12, 353–358. Moser, B.R., 2008. Influence of blending canola, palm, soybean, and sunflower oil methyl esters on fuel properties of biodiesel. Energy Fuels 22, 4301–4306. Moser, B.R., Vaughn, S.F., 2012. Efficacy of fatty acid profile as a tool for screening feedstocks for biodiesel production. Biomass Bioenergy 37, 31–41. Pienkos, P.T., Darzins, A., 2009. The promise and challenges of microalgal-derived biofuels. Biofuel Bioprod. Biorefin. 3, 431–440.

27

Rodolfi, L., Zittelli, G.C., Bassi, N., Padovani, G., Biondi, N., Bonini, G., Tredici, M.R., 2009. Microalgae for oil: strain selection, induction of lipid synthesis and outdoor mass cultivation in a low-cost photobioreactor. Biotechnol. Bioeng. 102, 100–112. Shirvani, T., Yan, X., Inderwildi, O.R., Edwards, P.P., King, D.A., 2011. Life cycle energy and greenhouse gas analysis for algae-derived biodiesel. Energy Environ. Sci. 4, 3773–3778. Sialve, B., Bernet, N., Bernard, O., 2009. Anaerobic digestion of microalgae as a necessary step to make microalgal biodiesel sustainable. Biotechnol. Adv. 27, 409–416. Sills, D.L., Paramita, V., Franke, M.J., Johnson, M.C., Akabas, T.M., Greene, C.H., Tester, J.W., 2012. Quantitative uncertainty analysis of life cycle assessment for algal biofuel production. Environ. Sci. Technol. 47, 687–694. Soh, L., Zimmerman, J., 2011. Biodiesel production: the potential of algal lipids extracted with supercritical carbon dioxide. Green Chem. 13, 1422– 1429. Spolaore, P., Joannis-Cassan, C., Duran, E., Isambert, A., 2006. Commercial applications of microalgae. J. Biosci. Bioeng. 101, 87–96. Stephens, E., Ross, I.L., King, Z., Mussgnug, J.H., Kruse, O., Posten, C., Borowitzka, M.A., Hankamer, B., 2010. An economic and technical evaluation of microalgal biofuels. Nat. Biotechnol. 28, 126–128. Subhadra, B.G., Edwards, M., 2011. Coproduct market analysis and water footprint of simulated commercial algal biorefineries. Appl. Energy 88, 3515–3523. Ugwu, C.U., Aoyagi, H., Uchiyama, H., 2008. Photobioreactors for mass cultivation of algae. Bioresour. Technol. 99, 4021–4028. U.S. Environmental Protection Agency, 2010. EPA lifecycle analysis of greenhouse gas emissions from renewable fuels. EPA-420-F-10-006. UTEX, 2011. The Culture Collection of Algae. University of Texas, Austin. Yang, J., Xu, M., Zhang, X., Hu, Q., Sommerfeld, M., Chen, Y., 2011. Life-cycle analysis on biodiesel production from microalgae: water footprint and nutrients balance. Bioresour. Technol. 102, 159–165.