Mass balance and life cycle assessment of biodiesel from microalgae incorporated with nutrient recycling options and technology uncertainties

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1 GCB Bioenergy (2015) 7, , doi: /gcbb Mass balance and life cycle assessment of biodiesel from microalgae incorporated with nutrient recycling options and technology uncertainties JUHONG YUAN 1, ALISSA KENDALL 2 and YIZHEN ZHANG 1 1 Institute of Transportation Studies, University of California, 1605 Tilia Street, Suite #100, Davis, CA 95616, USA, 2 Department of Civil and Environmental Engineering, University of California, 1 Shields Avenue, Davis, CA 95616, USA Abstract This article presents mass balances and a detailed life cycle assessment (LCA) for energy and greenhouse gases (GHGs) of a simulated microalgae biodiesel production system. Key parameters of the system include biomass productivity of 16 and 25 g m 2 day 1 and lipid content of algae of 40% and 25% for low and normal nitrogen conditions respectively. Based on an oil efficiency from wet biomass of 73.6% and methane yields from anaerobically digested lipid-extracted biomass of 0.31 to 0.34 l per gram of volatile solids, the mass balance shows that recycling growth media and recovering nutrients from residual biomass through anaerobic digestion can reduce the total demand for nitrogen by 66% and phosphorus by 90%. Freshwater requirements can be reduced by 89% by recirculating growth media, and carbon requirements reduced by 40% by recycling CO 2 from biogas combustion, for normal nitrogen conditions. A variety of technology options for each step of the production process and allocation methods for coproducts used outside the production system are evaluated using LCA. Extensive sensitivity and scenario analysis is also performed to provide better understanding of uncertainty associated with results. The best performing scenario consists of normal nitrogen cultivation conditions, bioflocculation and dissolved air flotation for harvesting, centrifugation for dewatering, wet with hexane, transesterification for biodiesel production, and anaerobic digestion of biomass residual, which generates biogas used in a combined heat and power unit for energy recovery. This combination of technologies and operating conditions results in life cycle energy requirements and GHG emissions of 1.02 MJ and 71 g CO 2 - equivalent per MJ of biodiesel, with cultivation and oil dominating energy use and emissions. Thus, even under optimistic conditions, the near-term performance of this biofuel pathway does not achieve the significant reductions in life cycle GHG emissions hoped for from second-generation biofuel feedstocks. Keywords: algae, anaerobic digestion, biofuels, carbon footprint, energy recovery, LCA, nitrogen, nutrient recycling, phosphorous, Scenedesmus dimorphus Received 3 December 2013; revised version received 21 August 2014 and accepted 12 September 2014 Introduction The issues of fossil energy depletion and global climate change due to the increase of greenhouse gas (GHG) emissions from combustion of fossil fuels have compelled researchers to seek renewable alternative fuels. Biofuels produced from agricultural starch, sugar and oil crops such as corn, sugarcane, rapeseed, soybean, and palm, so-called first-generation biofuels, have been produced at commercial scale worldwide. However, first generation biofuels requiring arable land have also received increasing criticism because of intensive fertilizer use and their contribution to direct and indirect land use changes, which cause additional GHG Correspondence: A. Kendall, tel , fax: , amkendall@ucdavis.edu emissions and other environmental impacts (Lapola et al., 2010; Lambin & Meyfroidt, 2011), and because of consequences affecting food availability and price, particularly in developing countries (Von Braun, 2007). Additional challenges for many crop-based fuels include significant freshwater consumption, which may compete with food and feed crops and increase water scarcity (Dominguez-Faus et al., 2009). Second and third generation biofuel feedstocks, such as many cellulosic crops and microalgae, are intended to avoid some of these shortcomings. Compared to conventional oil crops, microalgae have shown great potential because of their high yield ( l of algal oil per hectare of land, hundreds of times higher than oil yields per land area from soybeans, for example) (Chisti, 2007), and because microalgae can be grown on nonarable land and in wastewater, saline, or brackish water John Wiley & Sons Ltd 1245

2 1246 J. YUAN et al. In addition, microalgae also produce large amounts of biomass residual coproduct which may be used as an energy product or animal feed (Brennan & Owende, 2010). Efficient nutrient recovery from algal biomass and water recycling provide potential environmental and economic benefits for microalgae production, and have been identified as an essential step for viable algal cultivation at a large scale (Biller et al., 2012). In fact, nutrient demands may constitute as much as 26% of the life cycle energy requirements and 22% of GHG emissions of algal biodiesel production (Lardon et al., 2009; Stephenson et al., 2010). Therefore, it is important to explore nutrient recycling opportunities in and analyze the life cycle environmental impacts of algal biofuel production systems. Such an analysis can help policy makers and industry decide to what extent algal biodiesel is environmentally viable and how to save energy through nutrient recycling and other technology routes during the production. Two previous quantitative studies on nutrient balances of algal biofuel production have been found. R osch et al. (2012) quantified nutrient flows and nutrient recycling under various scenarios for processing algal biomass residues including anaerobic digestion, hydrothermal gasification and animal feed production. Alcantara et al. (2013) evaluated the nutrient, carbon and energy balances in an integrated algae cultivation and anaerobic digestion system. Significant nutrient recycling potentials are found in both studies, and the recycling rates for N and P can achieve as high as 70% and 89% respectively. In addition, quite a few of previous LCA studies have shown that algal biodiesel is energyand GHG-intensive to produce and may not perform significantly better than their petroleum counterparts (Brentner et al., 2011; Clarens et al., 2011; Khoo et al., 2011; Razon & Tan, 2011). This is particularly true for algal biofuels produced from photobioreactor systems or where drying is required prior to oil (Lardon et al., 2009; Stephenson et al., 2010; Sills et al., 2012; Soratana et al., 2012; Vasudevan et al., 2012). This study includes a mass balance analysis which tracks the flows of nutrient, water and carbon to provide a broad picture of mass flow and to identify co-benefits of nutrient recovery. Based on the mass balance, a LCA model has been developed which analyzes the primary energy requirement and GHG emissions from algal biodiesel production. Current technology options for each unit process are tested, along with different ways of utilizing coproducts. This study also discusses the issue of coproduct allocation methodology by comparing system expansion and value-based allocation methods. Materials and methods System definition This study develops a mass balance model focusing on nutrient, carbon, and energy flows through a microalgae biodiesel production system, and then uses this mass balance model to develop an LCA model. LCA assesses the environmental impacts of a production system by tracking environmental flows, including inputs of materials, resource and energy, and outputs such as air and water emissions over the entire supply chain and throughout a product s life cycle. The mass balance quantifies masses entering and leaving a chemical or physical process, which is necessary to simulate the algal production system. The system boundary and a schematic of the production processes are illustrated in Fig. 1. The following life cycle stages are included and constitute a cradle-to-gate assessment: algae cultivation, harvesting and dewatering, drying (optional), oil, conversion to biodiesel, utilization of biomass residual and transportation between sites. The processes of algae cultivation, harvesting and dewatering, drying, oil, and utilization of residual biomass occur within the same facility, from where the crude oil is transported to a nearby Fertilizers, water, CO 2, elec. Flocculants, elec., heat Elec., heat Solvent, elec., heat Methanol, elect., heat Elec., heat Algae cultivation Normal-N Low-N Legend Base case Alt. cases T = Transportation Harvesting & dewatering Biofloc + DAF & centrifugation Polymer, DAF, & centrifugation Alum, DAF, & centrifugation Centrifugation Drying No drying Thermal drying Oil Hexane Legend ScCO 2 T Conversion Esterification Coproducts: glycerin & digestate solids Biomass residual utilization No utilization (waste) Anaerobic digestion (Wet biomass) Direct combustion (Dry biomass) Fig. 1 System diagram for the algae-to-biodiesel production system with alternative technology options. Elec = Electricity, Biofloc = Bioflocculation, DAF = Dissolved air flotation, ScCO 2 = Supercritical carbon dioxide. (Black boxes show base-case technology routes and coproduct utilization assumptions, and alternative technology routes are shown below these black boxes).

3 LIFE CYCLE ENERGY AND GHG ASSESSMENT OF ALGAL FUEL 1247 biorefinery for biodiesel production. Some processes are excluded from this LCA, such as infrastructure construction, repair and maintenance of infrastructure and equipment, and waste management. The production system uses open raceway ponds (ORPs) for algae cultivation, and groundwater with light-to-medium salinity for the growth medium. As shown in Fig. 1, two nitrogen supply scenarios, Normal N and Low N, are included for algae cultivation. Four combinations of technologies for harvesting and dewatering are considered, including bioflocculation followed by dissolved air flotation (DAF) and centrifugation, flocculation with polymer followed by DAF and centrifugation, flocculation with aluminum sulfate followed by DAF and centrifugation, and centrifugation only. Thermal drying is required only when the dry process is used. Hexane and supercritical carbon dioxide (ScCO 2 ) are modeled for both wet and dry biomass. Although not listed in Fig. 1, a flash desolventizing system is included in the solvent process to remove the solvent from the algal oil and biomass residual. The only pathway for conversion of crude algal oil to biodiesel considered is transesterification. Three scenarios of biomass residual utilization are modeled: no utilization on site, on-site anaerobic digestion (AD) of wet biomass residual, and on-site direct combustion of dry biomass residual. AD produces biogas, suitable for use in a combined heat and power (CHP) unit, and digestates, from which the liquid fraction is recovered and fed into the algae ponds for water and nutrient recycling, and the solid fraction is composted and used off-site as a nutrient-rich soil amendment. Direct combustion of dry biomass generates heat and power, but no nutrients can be recovered. This study uses 1 kg of dry biomass as a modeling unit to assess the material and energy consumption in each unit process; however, the results are presented for 1 MJ of algal biodiesel (the functional unit) for comparing results with other LCA studies of algal biofuels or with conventional diesel. Unit process specifications, mass balance modeling and life cycle inventory (LCI) development Algae cultivation. This study uses the green microalgae Scenedesmus dimorphus, which has been identified as a good candidate for biofuel production in previous experiments because of its high lipid content (16% to 40%) and high biomass productivity (0.26 to 0.43 g l 1 day 1 at maximum, under optimal laboratory conditions) (Renaud et al., 1994; Rodolfi et al., 2009; Zhou et al., 2011; Adams et al., 2012). Algae are assumed to be cultivated in ORPs with a water depth of 0.3 m in this study. The most common competing cultivation systems, photobioreactors, are not economically viable and use more energy for operation than ORP systems (Stephenson et al., 2010). The facility is assumed to be located in southern New Mexico based on consultation with industry. The desert conditions of New Mexico make it suitable for algae cultivation, including rich sunlight, high average temperatures, and ample non-arable land (Sheehan et al., 1998). Algal growth can be affected by the amount of light, temperature, nutrient quality and quantity, ph, salinity, and other factors, and the daily and monthly growth rate vary significantly throughout the year. This study models average biomass growth rates for two N conditions based on previous mass balance and life cycle assessment studies; 25 g m 2 day 1 has been employed as in most studies (Lardon et al., 2009; Batan et al., 2010; Frank et al., 2011; Khoo et al., 2011; Soratana & Landis, 2011), and 16 g m 2 day 1 is used here to reflect the lower growth rates under Low N conditions (Razon & Tan, 2011). It is assumed that the N supply is constant for these two conditions. Although higher growth rates have been reported in laboratory studies under optimized culture conditions (e.g to 0.43 g l 1 day 1 ), the rates are expected to be much lower for large-scale industrial ORP systems. The lipid contents for algae grown under the two conditions are 25% (Normal N) and 40% (Low N) dry weight. For both conditions, C contained in algae biomass is about 50% dry weight, and P contained in biomass is about 1.29% dry weight calculated from the molecular composition. The N contents for algae biomass grown in Low N and Normal N conditions are 1.75% and 5.25%, calculated from the C : N : P ratio of 100 : 9 : 1 and 100 : 3 : 1 (see section S1 of Data S1 for additional detail) respectively. The nutrients requirement is directly based upon the elemental composition of algal cells. The theoretical requirement of CO 2 for producing 1 kg of biomass is 1.83 kg, derived from elemental composition of algal cells. The efficiency of CO 2 mass transfer to the ponds is assumed to be 83% based on previous literature (Putt et al., 2011), with the remaining lost to the atmosphere. Flue gas containing CO 2 from a hypothetical adjacent power plant is assumed to be delivered to the ponds in a direct injection process; 22.2 kwh of electricity is required to inject 1 metric ton of CO 2 (Kadam, 2002). No additional energy is included for CO 2 acquisition and delivery because this hypothetical power plant can supply CO 2 enriched flue gas continuously. The energy requirement for pumping water from the saline aquifer is MJ m 3 m-depth, adapted from Murphy & Allen (2011). In addition, electric energy is required for water pumping within the system before and after harvesting and dewatering, and for pumping of liquid digestates from the anaerobic digester back into the algal ponds under the scenario of AD for processing wet biomass residual. Assuming a 750-W pump with a flow rate of 15 m 3 per hour (Lardon et al., 2009), the internal pumping electricity requirement is then 0.18 MJ m 3. Electrical energy is also required to mix the culture medium with paddle wheels at a nominal water velocity of 25 cm s 1. The power required for paddlewheel mixing is W m 2 (Lundquist et al., 2010; Collet et al., 2011; Murphy & Allen, 2011). Water recycling can significantly reduce freshwater requirements for algae cultivation; however, the reuse of saline groundwater can increase the salinity of algal growth medium, which inhibits the biomass productivity. The highest salinity level algae can tolerate is assumed to be 3000 mg l 1 (Kaewkannetra et al., 2012). Thus, a desalination process, reverse osmosis (RO), is required to maintain acceptable salinity with water recycling. A small fraction of the groundwater (4% of the original volume) entering the ORPs is treated with RO. The electricity requirement for RO is 4 kwh m 3 of treated water (Raluy et al., 2006).

4 1248 J. YUAN et al. Algal ponds are subject to N losses via ammonia volatilization and N 2 O emissions, and water evaporation from the ponds. Approximately 4% of the total N influent is volatilized as ammonia. Additional information explaining the selection of these values is provided in section S2 of Data S1. N 2 O emissions from the ORPs are estimated at 0.002% of the N inputs (Fagerstone et al., 2011), substantially lower than the default IPCC emission factor (1% of N inputs) (IPCC et al., 2006). The evaporative loss of water from the ORP is 5.96 l per m 2 per day, calculated from class-a pan evaporation measurements for Luna county, New Mexico ( cm day 1, Western Regional Climate Center) which is converted to pond evaporation with a coefficient of 0.81 (Boyd, 1985). Table 1 summarizes the key parameter assumptions, algal cell compositions, and material and energy inputs under both N supply conditions. Harvesting and dewatering. Four technologies for harvesting are modeled as stated in the System Definition section. Bioflocculation (sometimes referred to as autoflocculation) does not require chemical flocculants and achieves high harvesting efficiency by changing culture conditions or using bio-polymers produced by algae to act as a flocculant. Bioflocculation for Scenedesmus sp. has been demonstrated in previous studies (Gonzalez et al., 1997; Kim et al., 2011; Gonzalez-Fernandez & Ballesteros, 2013; Agbakpe et al., 2014). The initial solids level before harvesting is 0.5 g l 1, which is increased to 50 g l 1 for biomass out of the DAF process, and 120 g l 1 for biomass out of the disc-stack centrifuge. The dosing of flocculants was collected from previous literature (Sim et al., 1988; Buelna et al., 1990; Molina Grima et al., 2003). The biomass out of the bulk harvesting stage is then sent to a decanter bowl centrifuge for dewatering, which increases the solids level to 180 g l 1 (Liang et al., 2012). A biomass removal efficiency of 90% is assumed for bulk harvesting using flocculation and DAF, and 96% for centrifugal dewatering (Uduman et al., 2010). To efficiently harvest biomass from the low-density microalgae culture, mixing is required for flocculation. The energy required for mixing is estimated to be kwh per metric ton of dry biomass (Lee et al., 2010). The electricity required for DAF is calculated based upon a manufacturer s technical documentation, assuming a 14 kw DAF with a flow rate of 60 m 3 h 1, and an energy efficiency of 70% (Toro Wastewater Equipment Industries, N.D.). The energy required for bulk harvesting with a disc-stack centrifuge and for dewatering with a decanter centrifuge is assumed to be 1 kwh and 8 kwh per m 3 of water into the centrifuge respectively (Mohn, 1980). Table 2 provides a summary of material and energy requirements for harvesting and dewatering. Drying (optional) and oil. Thermal drying of harvested algae is required if a dry process is chosen. The drying process increases the solids level from 180 to 900 g l 1, evaporating 4.44 kg of water per kg of dry biomass. The heat requirement is calculated by the theoretical heat requirement of MJ per kg of water evaporated (Lowe, 1995) divided by a thermal energy efficiency of 70%. In addition, the dryer requires MJ of electricity per kg of water evaporated to operate (Show et al., 2012). Algal lipids can be extracted from dry or wet biomass using hexane solvent or supercritical CO 2 (ScCO 2 ). Cell disruption using high pressure homogenization, which has been shown to be the most effective pretreatment process for lipid (Molina Grima et al., 2003), is required prior to wet. The theoretical energy requirement for processing 1 m 3 of algal slurry under kpa ( PSI) pressure is 69 MJ (Samarasinghe et al., 2012) and energy efficiency of the high pressure homogenizer is assumed to be 65%. Thus, 0.59 MJ kg 1 biomass is required for cell disruption. After, a flash desolventizating system is needed to remove over 99.99% of the solvent from the algal oil and lipidextracted residual biomass operating at a temperature of 75 C. Table 1 Elemental composition and material and energy requirements of algae biomass under two nutrient supply conditions Nutrient supply scenarios Low N Normal N Data sources Growth rate (g m 2 day 1 ) Assumptions in this study Lipid content (weight) 40% 25% (Gouveia & Oliveira, 2009) Protein content (weight) 11% 32% Calculated from N contents Carbohydrate content (weight) 41% 35% Calculated Ash (weight) 8% 8% (Nalewajko, 1966) C content (g kg 1 biomass) (Chisti, 2007) N content (g kg 1 biomass) Calculated from the C:N:P ratios P content (g kg 1 biomass) Make-up water (L kg 1 biomass) L/m 2 /day (Boyd, 1985; Western Regional Climate Center, 2013) Energy requirements Electricity for groundwater pumping (MJ kg 1 biomass) MJ/m 3 /m-depth (Murphy & Allen, 2011) Electricity for internal water pumping (MJ kg 1 biomass) MJ/m 3 (calculated from (Lardon et al., 2009)) Electricity for CO 2 injection (MJ kg 1 biomass) kwh/ton CO 2 (Kadam, 2002) Electricity for paddlewheel mixing (MJ kg 1 biomass) W/m 2 (Lundquist et al., 2010; Collet et al., 2011; Murphy & Allen, 2011) Electricity for water desalination (MJ kg 1 biomass) kwh/m 3 (Raluy et al., 2006)

5 LIFE CYCLE ENERGY AND GHG ASSESSMENT OF ALGAL FUEL 1249 Table 2 Material and energy requirements for harvesting and dewatering Bulk Harvesting Technologies Biofloc + DAF Polymer floc + DAF Alum floc + DAF Centrifugation Initial solids level (g l 1 ) 0.5 before harvesting Flocculants dosing 10 mg l 1 (Buelna et al., 1990) 92 mg l 1 (Benemann & Oswald, 1996) Electricity for mixing MJ kg 1 biomass (Lee et al., 2010) Electricity for DAF 0.12 MJ kg 1 biomass (Calculated) Electricity for centrifugal harvesting 0 1 kwh m 3 (Mohn, 1980) Harvesting efficiency 90% 90% 90% 96% Concentrated solids level (g l 1 ) after bulk harvesting Electricity for centrifugal dewatering 8 kwh m 3 (Mohn, 1980) Concentrated solids level (g l 1 ) 180 after dewatering Hexane separated out is cooled and recycled back to the extractor. A portion of the water is also separated out from residual biomass during the heating and cooling processes, and is recycled to the algae ponds after a reverse osmosis step. Hexane solvent is lost due to the residual hexane contained in algal oil and residual biomass and fugitive emissions during the and post- processes. Hexane loss was based on data from a soybean biodiesel life cycle study (Sheehan et al., 2000). The heat and electricity requirement for lipid from dry biomass with hexane is from a technology and engineering assessment of algae biofuel production for a large scale plant (Lundquist et al., 2010). Because of the lack of data for extracting oil from wet biomass, energy consumption is assumed to be proportional to the volume of processed materials, which is thus five times higher for wet than for dry for a unit of dry algae biomass. In addition, the efficiency for dry is 92%, which means that 8% of the algal oil (by mass) remains in the residual biomass. The efficiency is 73.6% for wet, 80% of the dry efficiency. Data for ScCO 2 is highly uncertain because this technology is not commonly used and because of the limited data. The electricity consumption during ScCO 2 from dry biomass, MJ per kg of oil, is derived from a life cycle study for soybean oil with the same method (Li et al., 2006). The electricity consumption during ScCO 2 from wet biomass was assumed to be 1830 kwh per 10 4 MJ of algal biodiesel (Brentner et al., 2011). The oil efficiencies for ScCO 2 from wet and dry biomass are both 95%, since the water content does not affect the yield of oil out of the ScCO 2 process (Soh & Zimmerman, 2011). Table 3 summarizes the material and energy requirements for drying and oil stages. Loss of N and P due to co- is assumed to be 1% in this study (R osch et al., 2012), and this fraction is tested over a range from 0.5% to 1.5% in the sensitivity analysis (see Data S1). Utilization of wet biomass residual. Scenarios for processing the lipid-extracted biomass residual include no utilization (landfilling), anaerobic digestion (AD) for wet biomass residual, and direct combustion for dry biomass residual. Wet algal biomass residual is sent to an anaerobic digester to produce biogas (a combination of mainly methane and CO 2 ) which is combusted to supply electricity and heat within the facility. Theoretical methane yields from anaerobically digested algae biomass can be as high as 0.47 to 0.8 L CH 4 /g volatile solids (VS), which can be calculated from the composition of microalgae, depending on the algal species (Sialve et al., 2009). However, only a portion of the organic matter in the algal body can be digested, and many factors can inhibit or slow the process, including initial hydrolysis of lipids, ammonia released from proteins, salt concentration, and other operational parameters such as retention time and temperature (Gonzalez-Fernandez et al., 2012). Previous studies have shown a yield of 0.1 to 0.56 l CH 4 /g VS for digestion of whole algal biomass (described in more detail in section S3 of the Data S1). In this study, CH 4 yield is calculated by multiplying the theoretical yield by a carbon degradation coefficient. The theoretical yield is based on the substrate composition (i.e., the lipid, protein, carbohydrate, and ash contents) and specific methane yield from each substrate given by Sialve et al. (2009). The carbon degrading coefficient is 52%, obtained from a study on methane production from continuous anaerobic digestion of Scenedesmus sp. with a hydraulic retention time of 58 days (Tartakovsky et al., 2013). The composition, theoretical yield and estimated realistic yield are shown in Table 4 for the two nutrient supply conditions. Nutrients contained in the liquid fraction of AD effluent are recycled to partly fulfill nutrient demand for algal cultivation and the solid fraction of AD effluent can be used for composting and land application outside of the production system. This study assumes that 65% of N is retained as ammonium in the liquid fraction of the digestate, and 35% of N in the solid fraction. The P recovery from AD of microalgae has not been clearly determined in previous studies; however, Alcantara et al. (2013) found that as much as 89% of P could be potentially recovered from the digestates of photoautotrophically cultivated algae. This high P recovery rate might be due to the

6 1250 J. YUAN et al. Table 3 Material and energy requirements for biomass drying and oil for 1 kg of biomass Wet biomass Dry biomass Technology options Hexane Extraction ScCO 2 Extraction Hexane Extraction ScCO 2 Extraction Drying Electricity for drying 1.23 MJ kg 1 biomass Heat for drying MJ kg 1 biomass Oil Extraction Extraction efficiency 73.6% 95% 92% 95% Hexane loss 12 g/kg biomass 2.4 g kg 1 biomass Electricity for cell disruption 0.59 MJ/kg biomass Electricity for 0.20 MJ kg 1 biomass 5.70 MJ kg 1 biomass 0.04 MJ kg 1 biomass 1.79 MJ kg 1 biomass Heat for 3.3 MJ kg 1 biomass 0.66 MJ kg 1 biomass Table 4 Biomass residue substrate characteristics, theoretical and realistic CH 4 yields Nutrient supply scenarios Low N Normal N Yield for each compound (L CH 4 /g VS) from (Sialve et al., 2009) Lipid content (weight) 15% 8% Protein content (weight) 15% 39% Carbohydrate content (weight) 59% 43% Ash content (weight) 11% 10% 0 Theoretical yield (L CH 4 /g VS) Digestability (VS Degradation) 52% 52% Practical yield (L CH 4 /g VS) significant accumulation of polyphosphates during the cultivation of algal biomass (Alcantara et al., 2013). This P recovery rate is adopted in this study. A review of other literature evaluating N and P recycling rates can be found in section S4 of the Data S1. AD requires 0.68 kwh of heat and kwh of electricity for 1 kg of biomass feedstock based on Collet et al. (2011). The resulting digestate must be separated into liquid and solid streams for recycling. The separation is conducted through a decanter centrifugation process, requiring 2.2 kwh of electricity per ton of digestate (Møller et al., 2002). Biogas is sent to a CHP unit without cleanup; all the carbon in the form of CH 4 and CO 2 contained in biogas is emitted as biogenic CO 2 after combustion. 50% of the energy from biogas is converted to heat and 36% of the energy is converted to electricity. The emissions of CO 2 are calculated from the carbon balance, and other air emissions for biogas combustion are taken from the EcoInvent database accessed through GaBi software (Ecoinvent Centre, 2011; PE International, 2012). 1.5% of biomethane produced from the AD process is lost as fugitive emissions during biogas generation and combustion (see section S5 of the Data S1 for more discussion of this parameter). Utilization of dry biomass residual. The lipid-extracted dry biomass residual is directly combusted in an on-site CHP unit (assuming to have the same electric and thermal efficiencies as those for biogas combustion) for internal energy use. The energy content of dry biomass residual varies from 12.6 to 13.4 MJ per kg, calculated based on the respective calorific value for the substrate components including lipid, protein, carbohydrate and ash (Lardon et al., 2009), and the substrate composition of biomass residual, affected by the N supply conditions and oil efficiency. The CO 2 emission is based upon the carbon content of biomass; the CH 4 emission, 0.03 g per MJ of fuel, is based on default values in the IPCC good practice guidance for non-co 2 emissions from stationary combustion (Penman et al., 2000); and the emission of N 2 O-N is assumed to be 0.7% of the fuel N (Hao et al., 1991). Ash produced from dry biomass combustion is not considered a coproduct here. Even when used as a soil amendment it rarely displaces any fertilizer use, and no specific benefits to the soil fertility have been found (Juarez et al., 2012). A summary of material and energy inputs and outputs from utilization of dry and wet lipid-extracted biomass residual is provided in Table 5. Coproduct treatment strategies: system expansion and economic allocation The production system generates coproducts of digestate solids from AD of wet biomass residual and glycerin from biodiesel production. Environmental burdens associated with the material and energy flows of a system that produces multiple products or services have to be allocated among the coproducts (Ayer et al., 2007). According to the International Organization

7 LIFE CYCLE ENERGY AND GHG ASSESSMENT OF ALGAL FUEL 1251 for Standardization (ISO), when subdivision of a system is not possible, system expansion is the next preferable option over value-based allocation methods. When system expansion cannot be used, allocation should be based on underlying physical characteristics of coproducts, or on their economic values (ISO, 2006). Both system expansion and economic allocation are modeled, since underlying physical characteristics (e.g., mass or energy content) are not really defensible criteria; algal oil is valued for its energy content while digestates may have value based on their nutrient content, and glycerin is widely used in pharmaceutical formulations. System expansion. System expansion presumes that a coproduct generated from the system will effectively displace other substitutable products in the market. By displacing other products, the environmental impacts of these substitutable products are avoided (Weidema, 2001). This method may not be feasible when an alternative function or process does not exist or when the inventory data for that alternative is lacking (Azapagic & Clift, 1999). In addition, it can introduce additional uncertainties by expanding the system boundary. Digestate solids and glycerin are assumed to displace synthetic fertilizers and synthetic glycerin, respectively. In this study, 1 kg of N and 1 kg of P from solid digestates can substitute for 0.6 kg of N and 0.4 kg of P in synthetic fertilizers respectively. The other coproduct, glycerin, displaces synthetic glycerin with a 1 : 1 mass ratio. The LCI data for production of synthetic fertilizers and glycerin are from LCI databases accessed through GaBi software (PE International, 2012). Economic allocation. Though criticism exists regarding valuebased allocation methods because they involve an arbitrary partitioning for the coproduction system, these methods have been widely used in LCAs for their simplicity and transparency; allocation based on economic values, in particular, has even been suggested as a baseline method for LCA (Guinee et al., 2004). The rationale for economic allocation is that an economic profit motive is the major driving force of most production systems (Gnansounou et al., 2009). Environmental burdens are allocated based on market prices of biodiesel and coproducts here. The price of biodiesel is assumed to be $4.3/gal, taken from the Clean Cities Alternative Fuel Price Report (U.S. DOE, 2013), and the price of composted solid digestates was estimated to be $44 per ton at the production site. The price of glycerin is modeled as $0.05 per pound (Yang et al., 2012), which is near the lower end of the market price of glycerin, as the market for glycerin is nearly saturated because of increasing production of biodiesel. Though the prices for these products are likely to fluctuate, the price ratios are assumed to be stable over a period of time. Study limitations Because of lack of data, the growth rates of algae biomass are assumed to be constant, 16 and 25 g m 2 day 1 for the two N conditions; however, this assumption may not reflect actual growth rates in practice. The algae biomass growth will vary with climate conditions, ph, light, and other culture conditions. Another limitation of this study is the exclusion of inoculum cultivation because of the assumption that algae seed strain is cultured and provided by a separate facility for large-scale operations, and because of lack of data. The nutrient requirement for inoculum cultivation could influence the results, though the influence is expected to be relatively small. In addition, the phosphorus recovery from anaerobic digestion of microalgae has been rarely addressed in previous studies; the value used in this study is significantly higher than phosphorus recovery from anaerobic digestion of other materials. Furthermore, this study did not consider the potential impacts of liquid digestate on light penetration in the ORP, nor how the residual hexane would affect the AD process, for the Table 5 Modeling parameters for wet and dry biomass residuals Parameters AD of wet biomass residual Direct Combustion of dry biomass residual Electricity input 0.39 MJ kg 1 feedstock (Collet et al., 2011) Heat input 2.45 MJ kg 1 feedstock (Collet et al., 2011) Methane yield 0.27 to 0.31 L CH 4 g VS (Low N) 0.33 to 0.34 L CH 4 g VS (Normal N)* CHP efficiency 36% electric efficiency, 50% thermal efficiency (Martens, 1998) Electricity output 1.7 to 2.2 MJ kg 1 biomass (Low N) 2.6 to 3.9 MJ kg 1 biomass (Normal N) 2.6 to 2.8 MJ kg 1 biomass (Low N) 3.5 to 3.6 MJ kg 1 biomass (Normal N) Heat output 2.4 to 3.1 MJ/kg biomass (Low N) 3.6 to 4.0 MJ/kg biomass (Normal N) 3.7 to 4.0 MJ kg 1 biomass (Low N) 4.8 to 4.9 MJ kg 1 biomass (Normal N) Digestate separation MJ kg 1 digestate (Møller et al., 2002) Nutrient recycling effects Liquid digestates provide recycled N and P Coproducts Solid digestates *The yield of methane depends on the substrate composition of the biomass feedstock which is based on cultivation conditions (low N and normal N) and the technologies. Electricity and heat outputs are for 1 kg of harvested biomass, and the numbers take into account the harvesting and dewatering efficiencies and the oil efficiencies. A range of energy output is presented for biomass grown under low N and normal N conditions; the lower boundary of this range presents the energy output from utilization of biomass residual where oil is extracted with ScCO 2, and the higher boundary of this range presents a scenario of using biomass residual where oil is extracted with hexane.

8 1252 J. YUAN et al. reason that the amounts of liquid digestate and residual hexane are not significant. Results Nutrient, water, and carbon balances The anaerobic digestion of wet algal biomass residual provides nutrient recycling opportunities and partly offsets N and P fertilizer demands. To better understand the nutrient flow and nutrient saving effects, nutrients are shown on a fractional basis in Figs 2 and 3 for N and P respectively. The losses and recycling rates are the same on a fractional basis for 1 kg of biomass regardless of Normal or Low N conditions. Because the conversion from algal oil to biodiesel occurs in a separate facility and it does not affect nutrient losses or recycling, the conversion process is not included in these results. As shown in Fig. 2 and Fig. 3, the supply of nutrients can be broken down to three sources: added fertilizers, recycled nutrients from growth medium after harvesting and dewatering, and recovered nutrients from the liquid fraction of digestate. Water recycling and nutrient recovery from anaerobic digestate can provide 66.2% and 89.7% of the total N and P requirements respectively. Nutrients alone do not characterize the key mass flows through the production system. Fig. 4 shows nutrients, water and carbon balances for 1 kg of dry biomass cultivated in Normal N conditions, where 52.5 g N and 12.9 g P are required to grow 1 kg of algal biomass (dry weight) g N and 9.8 g P are provided by the liquid fraction of anaerobic digestate. Nutrients are subject to losses due to pond emissions, co-, and unrecovered nutrients in the solid fraction of digestate. The solid fraction, which accounts for 26.5% N fertilizer 33.6% Recycled N 66.4% N losses to air 4.0% Algae cultivation harvesting 96.0% Oil 83.0% N in recirculated culture 13.0% N in digestate liquid 53.4% N losses to co- 0.8% Anaerobic digestion 82.2% N in digestate solids 28.8% Fig. 2 Nitrogen recycling and losses in the algal biofuel system (wet and anaerobic digestion for the residual biomass). P fertilizer 10.3% Recycled P 89.7% P losses to co- 0.9% Algae P in digestate cultivation Oil Anaerobic solids 9.4% harvesting digestion 100% 86.4% 85.5% P in recirculated culture 13.6% P in digestate liquid 76.1% Fig. 3 Phosphorus recycling and losses in the algal biofuel system (wet and anaerobic digestion for the residual biomass). of the total digestate by weight and contains 15.7 g of N and 1.2 g of P, is exported as a coproduct. Accounting for all the losses and recycling, requirements for makeup synthetic fertilizers are 18.4 g N and 1.3 g P. Water is lost during pond evaporation and in the solid fraction of anaerobic digestate which is still 60% water content after solid-liquid separation. The growth medium and unharvested biomass are recirculated to the pond after harvesting and dewatering processes. As a result, a volume of l of water is needed for 1 kg of biomass under Normal N conditions. The carbon balance is affected through losses of CO 2, CH 4, and C; CO 2 is lost from the ORP due to a limited CO 2 mass transfer efficiency, in the form of CH 4 in fugitive emissions from anaerobic digester, and in the form of organic C retained in the digestate effluent. For 1 kg of biomass cultivated in Normal N conditions, g of C can be recycled in the form of CO 2 in the flue gas out of biogas combustion. The overall requirement for CO 2 -C is 362 g. Material and energy flow for the life cycle of algal biodiesel production Results are presented for combinations of two N supply scenarios, two drying- scenarios (dry and wet ) and three scenarios for biomass residual utilization (no utilization, AD for wet biomass residual and direct combustion for dry biomass residual). The technologies employed in other life cycle stages such as harvesting and dewatering and oil are the baseline scenarios, i.e., bioflocculation followed by DAF for harvesting and centrifugation for dewatering, and hexane solvent for oil. Efficiencies of biomass harvesting and dewatering and oil are taken into account. Table 6 summarizes the key inputs and outputs throughout the life cycle of algal biodiesel production under the three different utilization scenarios for algal biomass residual. The

9 LIFE CYCLE ENERGY AND GHG ASSESSMENT OF ALGAL FUEL 1253 N: 18.4 g P: 1.3 g Water: l CO 2 -C: g CO 2 -C: g Water (evap) l NH 3 -N Volatilization: 2.2 g N 2 O-N Emissions: 0.0 g CO 2 -C Emissions: g Cultivation N: 29.2 g P: 9.8 g Water: 3.1 l CH 4 -C: g CO 2 -C: 58.1 g Algal density: 50 g/l CH 4 -C: 1.7 g Anaerobic digestion Biogas combustion Numbers are subject to rounding errors Biomass: 1 kg N: 52.5 g P: 12.9 g Water: 1999 l C: 500 g Biomass: 0.14 kg N: 7.1 g P: 1.8 g Water: l C: 68 g Harvest & dewatering Biomass: 0.70 kg N: 44.9 g P: 11.0 g Water: 3.9 l C: g (Solid digestate) Undigested biomass: 0.32 kg N: 15.7 g P: 1.2 g Water: 0.8 l C: g Algal density: 180 g/l Algal oil: 0.16 kg C: 95.7 g Biomass: 0.86 kg N: 45.3 g P: 11.1 g Water: 3.9 l C: 432 g Oil N: 0.5 g P: 0.1 g Legend: Processes Mass or volume of substances Mass or volume of lost substances Mass flow Losses Fig. 4 Nutrients, water, and carbon balances (under Normal N conditions) per kg of dry biomass for the scenario of wet hexane and anaerobic digestion of residual biomass. thermal drying process prior to oil is the dominant energy consumer for the dry scenario, which alone nearly offsets the energy outputs from biodiesel and combustion of biomass residual. Wet oil requires less energy inputs, though it yields less energy outputs compared to dry oil on the basis of 1 kg of biomass due to lower efficiency. In addition, due to the higher oil content of algal biomass, the low-n cultivation conditions consume less N fertilizers during cultivation, and yield higher energy outputs from biodiesel, though less energy production from biomass residuals is realized. Regardless of the pathways (wet or dry), utilization of biomass provides net energy benefits to the production system, with AD providing additional benefits through nutrient recycling to the ORP and generation of solid digestate as a coproduct. Life cycle primary energy requirement and GHG emissions for baseline, best and worst cases Based on the material and energy inputs and outputs for all combinations of different technology options in each life cycle stage, the life cycle impacts of scenarios are evaluated in terms of the primary energy requirement and GHG emissions, characterized as CO 2 -equivalent (CO 2 e) using 100-year global warming potential values from IPCC s Fourth Assessment Report (IPCC et al., 2007). Results are shown in Fig. 5 for the baseline, best and worst scenarios. The best and worst scenarios are determined based on life cycle primary energy requirements. Fig. 6 shows the distributions of energy requirement and GHG emissions to each unit process. The different strategies for utilization of biomass residual (no use at all and on-site use of biomass residual) reflect two different industrial philosophies, with one optimizing the production of the primary products and treating other streams out of the production system as waste, and the other minimizing low-value byproducts or waste, maximizing the reuse or recycling of byproducts within the system, and approaching a closed-loop system (Lee, 2012). As Fig. 5 shows, the best cases for the production systems with and without utilization of biomass residual are not the same. When biomass residual is not utilized, Low N cultivation conditions are the better choice than the Normal N conditions (baseline case in this study) in terms of life cycle primary energy requirement and GHG emissions, due to the higher oil content of biomass grown under Low N conditions and thus higher biodiesel energy yield (9.21 MJ/kg biomass compared to 5.75 MJ kg 1 biomass). When on-site utilization of biomass residual is not considered and when optimal unit processes are used, maximizing the yield of primary product and minimizing the production of coproducts (biomass residual) may lead to the best environmental performance. On the other hand, when residual biomass is used for energy and nutrients recovery, Normal N conditions for cultivation together with optimal technology for harvesting and dewatering and wet with hexane constitute the best case, due to the higher energy recov-

10 1254 J. YUAN et al. Table 6 Material and energy inputs and outputs for algal biodiesel production for 1 kg of dry biomass Normal N Low N Inputs and outputs Wet Dry Wet Dry Cultivation Fertilizer N (g) 18.4 (52.5)* (17.5) 17.5 Fertilizer P (g) 1.33 (12.9) (12.9) 12.9 Electricity (MJ) Harvesting and Dewatering Flocculant (g) 0 0 Electricity (MJ) Drying and Oil Extraction Hexane solvents (g) Electricity (MJ) for Heat (MJ) for Transport and Conversion Diesel (g) Electricity for conversion (MJ) Heat for conversion (MJ) Energy Outputs Biodiesel (MJ) Utilization of Biomass Residual (AD for wet, direct combustion for dry) Electricity for biomass processing (MJ) Electricity for digestate separation (MJ) Heat for biomass processing (MJ) Electricity (MJ) Heat (MJ) Material Outputs Solid digestates (kg dry wt.) Glycerin (g) Net Energy Benefits From Biomass Residual Utilization (Energy Production - Energy Consumption) Electricity (MJ) Heat (MJ) *Values in parentheses indicate fertilizer requirements when wet biomass residual is not utilized through anaerobic digestion. ery from AD which offsets significantly higher on-site energy demands compared to the cases where Low N conditions are employed for cultivation, and due to the higher nutrient savings from recycled liquid digestates. The worst case when biomass residual is not utilized consists of a combination of Normal N cultivation, energy-intensive disc-stack and decanter centrifuges for harvesting and dewatering, drying and ScCO 2. Though utilizing dry biomass residual through direct combustion to generate heat and power could offset primary energy requirements of 2.1 MJ and reduce 133 g of GHG emissions for 1 MJ of biodiesel energy output, the same combination of technology scenarios still constitute the worst case when recovery of energy from biomass residual is considered. Previous studies comparing technology options and incorporating uncertainty analysis have made similar observations regarding biomass drying (Sills et al., 2012). As Fig. 6 shows, harvesting and dewatering with centrifuges, drying with thermal dryers and oil with ScCO 2 are the dominant energy consumers and GHG emitters in the algae biodiesel production process. In addition, compared to wet with hexane solvents, wet with ScCO 2 contributes to more primary energy consumption and GHG emissions per MJ of biodiesel, even though this method can achieve much higher efficiency (95% compared to 73.6% for hexane ). The best case with utilization of biomass residual yields a life cycle energy requirement of 1.02 MJ and GHG emissions of 71 g CO 2 e per MJ of algal biodiesel as shown in Fig. 5. Algal biodiesel shows better environmental performance compared to conventional diesel, which requires 1.22 MJ of fossil energy and emits 96 g CO 2 e of GHG during the well-to-wheel life cycle (Frank et al., 2011). Algal cultivation and oil contribute most to the life cycle environmental impacts, and these two phases account for 86% of the primary energy requirement and 88% of the total GHG emissions when biomass residual is disregarded and no

11 LIFE CYCLE ENERGY AND GHG ASSESSMENT OF ALGAL FUEL 1255 (a) Algae cultivation (b) Low N Normal N Harvesting & dewatering Biofloc + DAF + Centrifuge Disc-stack & decanter centrifuges Drying & oil Wet hexane Dry ScCO 2 T Conversion Esterification Best case 2.35 MJ 143 g CO 2e Baseline 3.52 MJ 225 g CO 2e Worst case 7.93 MJ 499 g CO 2 e Algae cultivation Normal N Harvesting & dewatering Biofloc + DAF + Centrifuge Disc-stack & decanter centrifuges Drying & oil Wet hexane Dry ScCO 2 T Esterification Conversion Biomass residual utilization Anaerobic digestion Direct combustion Best case 1.02 MJ 71 g CO 2e Worst case 5.84 MJ 367 g CO 2e Fig. 5 Baseline, best and worst cases of technology combinations for all the unit processes throughout the life cycle of algal biodiesel production without utilization of biomass residual (a) or with utilization of biomass residual (b) for 1 MJ of algal biodiesel. The displacement method is used for co-products. coproduct is considered. The significant energy requirement is due to electricity use for groundwater pumping, internal pumping between ponds and paddle wheel mixing during the cultivation stage, and due to the heat requirement for hexane from wet biomass. The recovery of energy from AD of wet biomass residual can offset 79% of the total life cycle energy requirement and 75% of the GHG emissions. Comparison of alternative coproduct allocation methods All scenarios receive some coproduct credit due to the coproduction of glycerin during the transesterification step. If biomass residual is treated as waste or dry with direct combustion is chosen, no other coproducts are generated. For the wet process digestate solids are generated as a coproduct. Only the wet biomass residual utilization scenario is presented in Fig. 7; both Normal N and Low N conditions are included for the cultivation stage, and the best-performing technologies are used for harvesting and dewatering and oil. Not surprisingly, because the coproducts have low value and are produced in relatively low volume or amounts, the coproduct effect does not appear to be significant. Furthermore, economic allocation and displacement calculations yield similar results when credits are summed over both coproducts. It is then concluded that the uncertainty associated with the choice of allocation methods is low. Sensitivity analysis Sensitivity analysis is performed to understand how results can be affected with changes in parameter values. Six important parameters are chosen in this sensitivity analysis and an increase of 15 percent (optimistic) and a decrease of 15 percent (pessimistic) for each parameter are tested. The increase in any of these parameters leads to a decrease in both energy requirement and GHG emissions, and the decrease in any of these parameters leads to an increase in these results. These six parameters and the sensitivity analysis are shown in Fig. 8. As shown in Fig. 8, a 15% change in yield of CH 4 could result in a 35% change in primary energy requirement and a 32% change in GHG emissions. The results are also sensitive to harvesting and dewatering efficiency, CHP electric efficiency, lipid content, growth rate, and oil efficiency, in decreasing order. Additional sensitivity analysis results for parameters that affect nutrient recycling, including recovery rate from AD; lipid content of biomass; efficiency of harvesting, dewatering, and ; ammonia volatilization; and co- losses are provided in section S7, Figure S2, of Data S1.