Meta-analysis and Harmonization of Life Cycle Assessment Studies

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Critical Review

Meta-analysis and harmonization of life cycle assessment (LCA) studies for algae biofuels Qingshi Tu, Matthew J. Eckelman, and Julie B. Zimmerman Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b01049 • Publication Date (Web): 17 Jul 2017 Downloaded from http://pubs.acs.org on July 18, 2017

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Environmental Science & Technology

Meta-analysis and harmonization of life cycle assessment (LCA) studies for algae biofuels Qingshi Tu1, Matthew Eckelman2, Julie Zimmerman1* 1 2

Department of Chemical and Environmental Engineering, Yale University, New Haven, CT Department of Civil and Environmental Engineering, Northeastern University, Boston, MA

*

Corresponding author: Julie Zimmerman (Department of Chemical and Environmental Engineering, Yale University, 17 Hillhouse Avenue, New Haven, CT, 06511; Tel.: 203-4329703; Fax: 203-432-4837; Email: [email protected]).

ABSTRACT Algae biodiesel (BioD) and renewable diesel (RD) have been recognized as potential solutions to mitigate fossil fuel consumption and the associated environmental issues. Life cycle assessment (LCA) has been used by many researchers to evaluate the potential environmental impacts of these algae-derived fuels yielding a wide range of results, in some cases even differing on indicating if these fuels are preferred to petroleum-derived or not. This meta-analysis reviews the methodological preferences and results for energy consumption, greenhouse gas emissions and water consumption for 54 LCA studies that considered algae BioD and RD. The significant variation in reported results can be primarily attributed to the difference in scope, assumptions and data sources. To minimize the variation in life cycle inventory calculations, a harmonized inventory dataset, including both nominal and uncertainty data, is derived for each stage of the algae-derived fuel life cycle. Keywords: algae, LCA, biodiesel, renewable diesel, harmonization, meta-analysis

1.

INTRODUCTION

Microalgae (“algae” hereafter) hold great promise as environmentally preferable feedstock for biofuels. Major advantages include high photosynthetic efficiency and areal productivity, noncompetition with food production, and more efficient uptake of nutrients relative to land-based crops.1 These advantages mean that large-scale production of algae biodiesel or renewable diesel represents the most plausible path to complete substitution of petroleum diesel with a domestic, bio-based alternative.2 Biodiesel is a mixture of mono-alkyl esters derived from renewable oil feedstocks such as vegetable oil or animal fats via transesterification. Renewable diesel resembles the petroleum-based diesel in terms of fuel composition and properties (e.g. cetane number), and is typically derived from renewable oil feedstocks via hydroprocessing.3 Such an aspirational goal of 3rd generation (G3), or algal-derived, biofuels only makes sense if it does not place undue burden on the environment, and in particular, only if production and use of algae biofuels actually alleviate at a system level the energy, carbon and water consumption challenges 1

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presented by fossil fuels. Particularly for GHG emissions, regulatory programs such as Renewable Fuel Standard (RFS) and Low Carbon Fuel Standard (LCFS) are implemented to ensure the life cycle GHG emissions of biomass-based fuels (e.g. algae biodiesel and renewable diesel) meet required thresholds. Numerous efforts have been made to quantify the potential environmental benefits and impacts of algae-derived fuels using life cycle assessment (LCA). Studies have primarily examined total/fossil renewable energy consumption and/or greenhouse gas (GHG) emissions.4 Because of the nascent status of the algae biofuel industry, there are many competing technologies and process designs that have been evaluated using LCA.5-7 On the other hand, there has not been publicly available data from the few commercial algae biofuel companies. This situation has allowed for researchers to make divergent assumptions across a range of model parameters, particularly around algae cultivation inputs and management of coproducts. Differences in assumptions, process data, and the choice of system boundary make it difficult to compare the results among algae-derived fuel LCAs in any meaningful way, and unsurprisingly, the reported results vary widely. As an effort to understand and explore opportunities to harmonize the variability, several studies have reviewed the methodologies and results of the published LCA studies on algae biofuels. Handler et al.8 reviewed nine LCA studies for the environmental impact of algae cultivation in a raceway (RW). 24 growth scenarios were evaluated and the authors reported fossil energy demand, GHG emissions and freshwater consumption ranging from 0.4 to 47.2 MJ, 36 to 4,447 g CO2-eq, and 0.8 to 83.1 L, per kg algae biomass, respectively. The authors highlighted several factors that contributed to the variations, including the differences in scope of the study (e.g. inclusion of burden for infrastructure), assumptions (e.g. neglecting the burden associated with CO2 procurement), co-product allocation, and transparency of the modeling process (e.g. source of data). Similarly, Slade and Bauen9 reviewed seven of the nine LCA studies considered by Handler et al.8 and indicated that differences in assumptions about algae productivity and composition were a major contributor to the variation in the reported results. The authors also concluded that water management, CO2 handling, nutrient supply and infrastructure were the major contributors to the energy and environmental impacts of algae biofuels production. Menten et al.10 applied meta-regression analysis to 47 LCA studies to study the impact of technical data, methodological choice and typology (e.g. year of publication, location) of the study on the GHG emissions from different scenarios for the production of algae-derived fuels. The independent variables for the regression included system boundary, allocation method, generation of biofuels (i.e., 1st generation (G1) – sourced from sugars, starches, and vegetable oils; 2nd generation (G2) – sourced from lignocellulose; or 3rd generation (G3) – sourced from algae) and type of co-products. The GHG emissions associated with producing G3 biofuels varied by region with ranges of 41.6 to 136.2 (mean=88.9), 76.6 to 224.7 (175.3) and -21.6 to 22.1 (2.2) g CO2-eq/MJ for the globe, Europe and North America, respectively. The regression results indicated that the impact of algae productivity and oil concentration on GWP of algae biofuels are non-linear, and that the GHG emissions associated with using a photo-bioreactor

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(PBR) system were statistically higher than that of a raceway (RW) system. More recently, Quinn et al.11 reviewed different algae growth data and conducted a resource assessment for the scalability of algae biofuels finding a range of 2.3 to 136.9 m3/ha/yr for lipid productivity and 95.7 to 534 g CO2-eq/MJ for biofuel production. Similar to previous analyses of this type, the authors concluded that different productivity assumptions, technology choices, and system boundaries were the major contributors to the variability in the reported LCA results. Finally, Collet et al.4 reviewed 41 LCA studies of algae-derived fuels and identified several issues related to scope definition and inventory calculations. For example, water consumption is a less investigated impact indicator (15% of the studies). Also, the inclusion/exclusion of infrastructure is inconsistent (e.g. 44% of RW studies included the impact of infrastructure). Some important inventory calculations are neglected by most studies, e.g. only 6% and 28% of the studies considered the loss of N during cultivation and fugitive CH4 emission from anaerobic digestion step, respectively. Collectively, these previous reviews of LCA of algae-derived fuels have shown that the significant variations in results are due to the inconsistency in: (1) scope definition (e.g. system boundary, functional unit), (2) assumptions (e.g. using constant values vs. random values from empirical distributions), (3) technological choices (e.g., different process trains) and (4) data sources. In the past, harmonization efforts were exerted on reducing the inconsistency on the scope definition. Farrell et al.12 developed a meta-model to adjust six reported energy balance models for corn ethanol, adding processes that were excluded by some authors while omitting some extraneous variables, and using a consistent set of allocation rules across all studies. This reduced the range of net energy results by 26% and that of GHG emissions by 17%, resolving several important inconsistencies. For algae biodiesel, Liu et al.13 harmonized the system boundary, functional unit, upstream burdens and co-product offsets for six LCA studies, which reduced the ranges of energy consumption and GHG emissions by 83% and 76%, respectively. However, to the best of our knowledge, comprehensive harmonization of inventory data and associated modeling assumptions has not yet been performed for algae biofuels. In order to further reduce the variation, enabling a more accurate understanding of the potential benefits, impacts, and hotspots of fuels derived from algae, such a harmonized database is needed. This requires the development of a comprehensive summary of the key life cycle inventories and the associated assumptions for different technological options (e.g. energy use for RW operation, CO2 delivery). Additionally, as uncertainty analyses are becoming an integral part of algaederived fuel LCA studies,6 it is important to also catalog the extent to which uncertainties were quantified and incorporated in existing LCA studies. As such, this study aims to review methodological preferences of the existing studies and to conduct a quantitative summary of the energy consumption, GHG emission and water consumption results from existing results. From this, a harmonized life cycle inventory database from the reviewed studies, based on the evaluation of key LCIs and assumptions, is developed.

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This harmonized database reduces the variability caused by inconsistent methods and assumptions, and can be used to find a process train's central tendency for energy consumption, GHG emission and water consumption.14

2. METHODOLOGY 2.1 Screening the literature This study reviewed 77 LCA studies associated with algae as a feedstock for fuel production published between 2009 and 2016. The reference pool was reduced to 54 studies (Table S39) by excluding the studies from which numeric results could not be retrieved for any of the three indicators: energy consumption (cumulative, renewable or fossil energy), GHG emission and water consumption. As proposed by Davis et al.15,16 a full life cycle for algae biofuels production is defined as consisting of upstream supply (e.g. CO2 from outside the algae farm), algae cultivation, harvesting, lipid extraction, fuel upgrading and defatted algae treatment stages. Although some of these 54 studies did not investigate a full life cycle for algae biofuels (e.g. stopping at algae cultivation or oil extraction stages), they were included for purpose of harmonizing the inventory datasets.

2.2 Harmonization of reported impact results (system harmonization) Harmonization is an approach to minimize the difference in scope, assumptions, data sources and calculation procedure for life cycle assessments of the same products or processes conducted by different researchers. There are two types of harmonization procedures: system harmonization and technical harmonization.14 System harmonization is applied to the reviewed studies to alter the function unit, system boundary, and to define the consistent impact indicators for energy consumption, GHG emission and water consumption. Here, the function unit of the reviewed studies were changed to 1 MJ of biofuel/algae oil and the reported results were converted accordingly using the relevant constants in Table S1. The system boundary was altered to exclude the transportation between algae farm and fuel production facility, transportation for fuel distribution and fuel combustion, as most studies did not include those processes. In addition, the impacts related to infrastructure manufacturing and installation were removed, when the disaggregated data was available. System harmonization brings different studies to a relatively common basis and makes it feasible to isolate the variations in results caused by the different choices of technologies (e.g. hexane extraction vs. Sc. CO2 extraction). In this study, the energy consumption results from the literature are converted into net cumulative energy demand (CED) that is defined as total energy consumed (MJ), both fossil and renewable, for producing 1 FU of biofuel. The unit of GHG emission (g CO2-eq) and water consumption (m3) are consistent among all studies, and hence are also used for this study. The choice of characterization factors (CF) for quantifying GHG emissions can also be a source of variation. For example, there are two CFs of GWP100 for CH4 (w/ or w/o considering climate-carbon feedback) in IPCC Fifth Assessment

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Report.17 As most of the reviewed studies did not mention the CFs used, harmonization of CFs was not performed in this study. 2.3 Harmonization of key life cycle inventory data (technical harmonization) Technical harmonization is focused on the inventory and calculation aspects of the LCA. Technical harmonization performed in this study includes: (1) disaggregating life cycle inventory data to the unit process level, (2) harmonizing the calculation procedures (e.g. using first principles or engineering equations), (3) filling in the missing processes that were not originally reported (e.g. upstream burdens for CO2 delivery), and (4) correcting the dependency between precedent and subsequent processes (e.g. relating the CH4 generation rate with defatted algae composition). “In contrast to system harmonization, which is applied to the reviewed studies, technical harmonization is applied to create a harmonized database for future algae biofuel studies.” The examples of technical harmonization are summarized in Table 1. Table 1. Examples of technical harmonization for each stage Disaggregating inventory data

Harmonizing calculation procedure

Upstream

Categorize the energy consumption data for CO2 procurement by CO2 sources (e.g. treated flue gas, direct injection, virgin CO2 from chemical production)

Cultivation

Convert pond mixing energy consumption from "kWh/kg algae" to "kWh/m2" to reduce the uncertainty range of reported results

Identify the representative model for calculating energy for CO2 transport from source to algae farm (e.g. low-presssure transport model from GREET)18 Harmonize the calculation procedure for the CO2 and fertilizer demand (e.g. using Redfield ratio to determine the carbon concentration of dry algae to estimate the theoretical CO2 demand) Harmonize the dataset for harvesting yield and the resulting algae concentration (in water) for different technologies (e.g. mode of the reported values of harvesting concentration after centrifuge) Harmonize the dataset for extraction yield for different technologies

Harvesting

Extraction

Unit conversion to reduce the uncertain range of reported results, similar as the example in Cultivation

Filling missing data/processes

Correcting dependency between precedent and subsequent processes

Include energy-based allocation between power plant and algae farm for the impact from processing flue gas30

CO2 transport from source (e.g. power plant) to algae farm: link the energy consumption to CO2 concentration and pipeline dimension (GREET)18

Include state-level consumptive water data for electricity generation.21

Link the state-level algae productivity in RW to local insolation and temperature19

x

x

Process simulation for Sc.CO2 extraction in Aspen Plus

Create regression model for estimating products yield from HTL hydroprocessing energy consumption modeling: "product compositionbased" calculation6 instead of "random sampling" from a range of empirical data CH4 generation rate modeling: "defatted algae composition-based" calculation20 instead of "random sampling" from a range of empirical data

Upgrading

Identify the representative model for estimating product composition from hydroprocessing.6

Process simulation for insitu (direct) transesterification in Aspen Plus

Defatted algae treatment

Create calculation procedure for digestion liquid recycling to offset external fertilizer and water demand

Include carbon sequestration rate for digestion solid application (GREET)18

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2.4 Statistical assessment Descriptive statistics, such as mean, mode, and interquartile range (IQR), were used to show the central tendency and variation of the harmonized results. Fitted distributions and associated goodness-of-fit tests were performed using JMP® Pro 11 (www.jmp.com).

3.

RESULTS AND DISCUSSION

3.1 Summary of included studies The variations of overall results are substantial for all three indicators among studies reviewed (Figure S1). The literature results are grouped by design factors in Figure S2 and S3. Each row represents a design factor (X1-12) that constitutes the process train and columns (from left to right) represent total energy consumption, GHG emissions and water consumption, respectively. The wide ranges and number of outlier data points (circles) indicate significant variations among studies, even under the same choice of technology (e.g. RW). This could be explained by the difference in one or more of the other design factors of the process trains, or could be due to the difference in assumptions and inventory data applied by different author groups when exactly the same process trains were studied. Identifying the exact causes of the variations is beyond the scope of this study and the following discussion is intended to describe the general patterns observed. 3.2 Review of methodological preferences Scope Most of the reviewed studies used an energy based functional unit (FU), such as 1 MJ of biofuel. Other FUs include vehicle kilometers traveled (VKT), or volumetric/mass units. The choice of associated constants (e.g. fuel density, vehicle engine efficiency, heating value) may contribute to variability in reported impact results. Twenty-two studies applied a “cradle-to-gate” system boundary, where the life cycle starts with algae cultivation and ends with the production of biofuel at the refinery gate. Ten studies extended the system boundary to include fuel distribution (“cradle-to-pump”) and 16 studies included the fuel combustion stage (“cradle-to-wheel”). For a consistent comparison, the impacts associated with fuel distribution and combustion are removed from the original results, when the disaggregated data is available. The rest of the studies limited the system boundary to a certain stage (e.g., algae cultivation, dried biomass) before fuel production. The impacts from infrastructure (i.e., material and energy consumption associated with the construction of algae cultivation system) were typically considered minimal and thus were explicitly considered by only 18 studies. For the studies that included infrastructure, impacts were often estimated by using the relevant unit processes from EcoInvent database (www.ecoinvent.org). Energy consumption is the most studied impact category and 349 data points were retrieved for the energy necessary to produce either algae (BioD) or (RD). However, many different energy consumption indicators are used in the literature and the majority difference lies in the inclusion/exclusion of renewable energy consumption. For example, only 9

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studies considered energy return on investment (EROI) as an indicator, but four studies defined EROI as “energy output from product divided by total non-renewable energy input” while the other five studies did not mention the source of the energy input. Moreover, ratio-based indicators, such as net energy ratio (NER) and fossil energy ratio (FER), often do not have a consistent definition of the numerator and/or denominator. 43 out of 54 studies explicitly mentioned the application of allocation, 34 of which used system expansion. The co-products, such as glycerin, defatted algae biomass or digestion solids, were assumed to displace the counterparts based on the same functionality (e.g. equivalent energy content, protein content).22,23 Configuration of the life cycle process trains The configuration of a specific algae biofuel life cycle process train can be characterized by 12 “design factors” that cover the major technical options for different stages. The frequency of each technical option used by the reviewed studies are summarized in Table S2. Many studies did not mention a specific algae species (23 of 54 studies). Chlorella sp. and Nannochloropsis sp. were investigated by 15 and 16 studies, respectively (some studies investigated more than one species). Most studies (40 of 54) investigated algae growth in a RW and 40 studies assumed nutrient sufficient situations. Unprocessed flue gas from a nearby source, e.g. a power plant or chemical plant, was assumed by most studies (35 of 54 studies) as the source of carbon dioxide during cultivation. Approximately half of the studies reported a US context with varying growth locations. There is no dominant preference of either auto(bio)-flocculation or chemical flocculation, and centrifuge is the primary choice for 2nd dewatering step. The most frequently investigated technologies for lipid extraction, fuel upgrading and defatted algae treatment are wet hexane extraction, biodiesel production from conventional transesterification and anaerobic digestion. It should be noted that the choice of technical option for drying, fuel upgrading and defatted algae treatment are contingent upon the technology chosen for lipid extraction. Therefore, these design factors should be considered collectively, when constructing a process train. Uncertainty analysis Twenty studies conducted sensitivity analysis to evaluate potential variation of the results caused by the uncertainty in certain assumptions and inventory data. In a sensitivity analysis, the value of one variable is changed while the others are kept at their nominal values. Eight studies performed stochastic modeling, using Monte Carlo simulation, to evaluate the uncertainty of the results by simultaneously changing the values for all variables of interest. The major variables of interest for uncertainty analysis include: algae productivity, lipid concentration, CO2 concentration, nutrient utilization rate, energy consumption for cultivation and harvesting, extraction yield and biogas yield. It is noteworthy that none of the reviewed studies applied statistical inference to compare the impact results between two different life cycles (process trains). Considering various degrees of uncertainty in the inventory data, overlap of impact

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results between two life cycles is expected when stochastic modeling is conducted. Therefore, statistical inference is recommended, for example, to determine the probability that two life cycles are significantly different. Transparency Separating the “noises” (e.g. variation caused by different assumption of the same technology) from inherent certainties (e.g. variation in process efficiency of the technology due to nature of the technology) is one of the grand challenge facing not only algal biofuels but the entire LCA community. Hence, transparent inventory data sets that allow the inspection of underlying assumptions, technical details, spatial and temporal conditions, are crucial for addressing this challenge.24 A four-level ranking system was devised to evaluate the transparency of the reviewed studies based on the disclosure of details associated with life cycle inventory calculation (Table S39). The level of transparency decreases (Lv.3 to Lv.0) when the amount of missing information regarding source of data and/or inventory calculation procedure increases. For example, eleven studies are labeled with highest transparency (Lv.3), because the highest level of details are disclosed. Such details include assumptions, equations and data sources for algae growth model, product yield and energy consumption. This level of transparency allows other researchers to understand the conditions from which the inventory data is derived, which enables a more accurate interpretation and ideally reproduction of the literature results. Fifteen studies provided detailed inventory data at unit process level (Lv.2). Examples are electricity consumption rates for RW pond mixing and algae dewatering. It should be noted that the unit process level data is often tied to certain conditions. For example, electricity for pond mixing, if reported in kWh/kg algae, is contingent upon the paddling speed, pond dimension and algae productivity. Therefore, caution should be taken to understand the relevant conditions, when intra/extrapolating the reported data. Fifteen studies are labeled with Lv.1 transparency, as the inventory data is reported in aggregated forms, e.g. total electricity consumed for algae cultivation. The inventory data with this level of transparency is recommended only for screening level analysis, as the information for disaggregating the data down to the unit process level is often not available. The rest of the reviewed studies report partial/no inventory data (Lv.0 transparency). In addition, 24 studies mentioned the use of EcoInvent database, however, only 3 studies provided the name of actual processes (e.g. “Electricity, medium voltage, at grid [US]”) used. This is particularly important because the difference in geographic location (e.g. US vs Switzerland) and system boundary (e.g. “at plant” vs “market for”) may lead to considerably different impact results. Also, disaggregating the impacts from infrastructure (e.g. biodiesel plant) is only feasible when the actual processes are provided. In order for a more accurate interpretation of the inventory data, it is recommended that future algae biofuel LCAs are performed with a high transparency (Lv.3 or Lv.2). 3.3 Quantitative review of the LCA results The impact results are grouped based on fuel type in Table 2. As a minimum 50% reduction in GHG emission (compared with 2005 baseline emission for petroleum diesel) is required by Renewable Fuel Standard (RFS), 16.6% and 24.6% of the algae BioD and RD LCA results, respectively, suggest that these fuels could be qualified for the standard. A quarter of the results show that cumulative energy demand of BioD and RD are equal or lower than petroleum diesel. Although RW systems are subject to significant evaporation loss, the majority of the studies still 8


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reported a lower normalized water consumption value as compared to petroleum diesel, which could be explained by the high fuel productivity from algae in general. The large values of standard deviation and interquartile range (IQR) indicate significant variations in the results for both BioD and RD, which can be explained by two reasons. Firstly, as multiple technical options available for each design factor to construct an algae biofuel life cycle, different choices of technical options for the same design factor (e.g. centrifuge vs belt press for 2nd dewatering step) could lead to considerably different impacts. This effect is propagated when more than one design factor is different between studies. Secondly, differences in assumptions (e.g. best/likeliest/worst scenarios) and inventory data of choice further confound the variations. Therefore, a harmonized set of assumptions and inventory data are needed in order to unambiguously compare the effect of different design factors (e.g. transesterification vs hydroprocessing) alone. In addition, “by-stage” results (i.e., impact results of individual life cycle stages) should also be reported, in order to the isolate these effects from overall results. This is particularly important, as overlap is observed between biodiesel and renewable diesel for all three indicators (e.g., energy consumption, GHG emissions, and water consumption). However, as shown in Table S38, few studies reported by-stage results. Table 2. Descriptive statistics of the reviewed studies (overall results for the three indicators) All data_BioD All data_RD CED GHG H2O CED GHG (MJ/MJ (g CO2-eq/ (m3/MJ (MJ/MJ (g CO2-eq/ fuel) MJ fuel) fuel) fuel) MJ fuel) -5.67EMin -21.0 -2642.5 -23.2 -4300.0 2 Max 97.2 9256.0 1.02 84.1 4000.0 Mean 15.4 1102.9 4.98E-2 6.8 315.9 Median 2.9 184.1 1.68E-2 2.3 137.9 Standard deviation (std) 23.5 1832.7 1.34E-1 15.7 1079.9 5th percentile 95th percentile IQR % of results ≦2005 baseline diesela % of results ≦ 50% of 2005 baseline diesel GHG emission # of data pt included

324 325 326 327 328 329 330 331

0.1

-45.5

1.12E-4

-0.5

-504.0

67.6 21.6 26.7%

5043.5 1602.8 30.2%

1.52E-1 4.68E-2 92.6%

43.0 3.5 26.6%

2538.0 290.7 40.6%

16.6% 270

235

H2O (m3/MJ fuel) -1.00E2 7.12E-1 1.30E-1 7.20E-2 1.67E-1 -3.99E3 5.06E-1 1.24E-1 61.3%

24.6% 81

79

69

31

a

2005 baseline diesel: CED=1.19 MJ/MJ; GHG= 93.08 g CO2-eq/MJ; H2O=0.11 m3/MJ

3.4 Harmonization of key life cycle inventory data Harmonization was performed by investigating the technological options, associated assumptions and inventory datasets adopted by the reviewed studies (Table 1). The harmonized inventory datasets, both nominal and uncertainty, and the associated assumptions are tabulated in the following subsections going from upstream stages (e.g., CO2 and water supply) to cultivation, harvesting, extraction, and upgrading as well as co-products/credits (where appropriate). The 9



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nominal and uncertainty datasets consist of single values, probability distributions and models that are most frequently used by the reviewed studies or justified to be suitable for the relevant purpose (e.g. peer-reviewed algae growth model for RW). Using a harmonized nominal dataset will eliminate the variation in inventory calculations for the same technology/unit process. Similarly, when sensitivity analysis or Monte Carlo simulation (e.g. for CO2 utilization rate) is necessary, using a harmonized uncertainty dataset will avoid the erroneous results caused by the outlier data points from the un-harmonized uncertainty range. Upstream stage (CO2) The upstream stage involves the procurement and delivery of CO2 and water for algae cultivation, which was explicitly discussed by some of the reviewed studies (24 of 54 studies). For example, the energy for procurement and delivery of CO2 ranged from 2%2 to 25%25 of total energy input (energy credits such as electricity from burning biogas are excluded), due to differences in system boundary, process train configuration, assumptions and data sources. In order to avoid a biased conclusion, the upstream energy burden (and associated GHG emission) should be included in the algae biofuel LCAs. Table S3 shows the reported energy burden for the procurement of virgin CO2 from chemical (e.g. ammonia) production and pure CO2 from treated flue gas (e.g. by MEA sorption). No procurement energy burden is considered for the untreated flue gas (e.g. 15% CO2). As suggested by the majority of the studies, chemical sorption (e.g. MEA) is the most frequently used technology for retrieving pure CO2 from flue gas. The most frequently cited data for MEA sorption is from Kadam et al.26 It is noteworthy that most studies did not include the steam use when citing Kadam et al.26 When the steam use is included, the total energy consumption becomes several times higher. The other two frequently cited data sources are GREET model18 and Clarens et al.28 As no justification can be made to recommend a single data source, a triangular distribution should be used to represent the min, max and likeliest values from all three data sources.29 In addition, the energy burden for flue gas treatment should not be solely allocated to algae cultivation, as removing CO2 from flue gas also benefits the power plant (i.e., less GHG emission). Therefore, the total energy consumption for flue gas treatment should be allocated between the power plant and algae cultivation facility, based on their energy output (Table S4), as suggested by Clarens et al.30 Pipeline transportation is the most commonly used method for delivering the CO2 stream to the algae cultivation facility. The major energy use for the delivery is to compress the CO2 stream to the desired pressure and the reported values are summarized in Table S5. The value from Kadam et al.26 is most frequently cited by other studies. However, as it is a lumped value based upon many embedded assumptions, its use is recommended for a screening-level LCA only. More accurate estimations can be obtained from Eq.S1, which represents a “compression pressure model” (CP model) that is used by many researchers.6,7,13,30 “Low-pressure pipeline transport

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model” (LPPT model; section S2) used in the GREET model is another calculation approach. As compared with CP model, the LPPT model explicitly accounts for the additional energy consumption caused by the friction along the pipeline, which provides a more realistic estimation. However, detailed information about the design specifications (e.g. gas flow rate, friction factor) are required for LPPT model, which may limit its usability. Upstream stage (water and nutrients) The power required for delivering water from source to algae cultivation site can be calculated based on the total head loss (Eq. S7 and S8). Table S8 summarizes the results reported by several research groups.6,7,31 As the choice of design specifications has a significant influence on the energy consumption, the information of the algae cultivation facility (e.g. water source, any required treatment, transportation distance, required water flow rate, pump efficiency) should be clearly specified for an accurate estimation of the energy for water delivery. Upstream burdens from nutrient manufacturing could account for a sizable portion of energy input. For example, Brentner et al.5 found the contribution of nutrient manufacturing was 7% and 11% under base and best scenarios of their study. Clarens et al.30 reported that upstream energy burden for nitrogen fertilizer manufacturing accounted for 44% and 56% of total energy input, depending on the process train configurations. Although various types of nutrients (e.g. urea, ammonium nitrate, diammonium phosphate, monocalcium phosphate) were investigated by different studies, the inventory data for those nutrients were relatively consistent, as most of them were from EcoInvent database. Cultivation The literature review shows that cultivation stage accounted for 4% to 57% of total energy input of the algae biodiesel life cycle. Similar to upstream and rest of the stages, this significant variation was caused by the differences in system boundary, process train configuration, assumptions and data sources combined. For example, the 4% contribution case corresponds to the situation where Sc.CO2 extraction dominated the energy input (85%).2 All 29 studies that explicitly or implicitly reported water consumption results showed that cultivation stage dominated the total water consumption (>90%), which agrees with the findings from a previous review.32 As very limited by-stage data are available for GHG emission, water consumption, and algae renewable diesel. The discussion on literature values is limited to energy input for algae biodiesel for rest of the stages. Algae productivity As 44 of 54 studies assume algae growth in RW under no nutrient limitation, the analysis of existing productivity results is also focused on this configuration. The reported values are categorized by species and data source (Table S9). The productivity results tend to center at 25 g/m2/d under the category of “assumed/other reference”. It should be noted that this average

11



412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451

Page 12 of 31

value neglects the location-specific climate information, hence may only be used for analyses with a low geographic resolution (e.g. national average). On the other hand, “pilot/industrial data” is preferred, as it reflects the actual algae growth under a specific climate condition, although its availability is still very limited. As an alternative, growth models may be a viable option to estimate location (climate)-specific productivity results. Here, two sets of modeled results are recommended to represent “low” and “high” estimations. The “low” estimation data is calculated from the growth model proposed by Wigmosta et al.19 The details of the model are depicted in Eq. S9-S13. The state-level solar radiation data was obtained from NREL’s solar summary data sheet. The temperature data (1971-2000) from National Ocean and Atmospheric Administration (NOAA)33 was used as a proxy for water temperature in RW. Other model parameters are summarized in Table S10. Table S11 shows the modeling results for 48 continental US states under three scenarios, and the detailed discussion can be found in section S4. The “high” estimation results in Table S11 are from Murphy and Allen34 and the values are 2.74 times of the results from “low” estimation model (Scenario A) on average. The calculation procedure and data source of the two models are not harmonized for this study, as limited details of the “high” estimation model are disclosed. Demand and input of CO2 and nutrients In cultivation stage, the term “demand” refers to the exact amount of CO2 and nutrients that are consumed by algae growth, while “input” refers to the total amount of those materials needed during cultivation. The difference between the two amounts is due to the material utilization efficiency that is affected by a combination of factors, such as mixing rate, temperature, surface area, etc. Most studies estimated CO2 and nutrient demands based algae stoichiometry and the most commonly used the formula is Redfield ratio (C106H181O45N15P). The range of CO2 input reported in the existing studies is 1.2-2.2 g (mode=1.9) CO2/ g dry algae and the CO2 utilization rate is 50%-95% (mode=90%). Urea and diammonium phosphate (DAP) are the most frequently used fertilizers for nutrient input. Several studies have mentioned the nutrient utilization rate23,3537 and the range is 75%-90%. When wastewater is used as the growth media, background nutrients can help to reduce the input of fertilizers. The detailed summary for CO2 input, utilization rate and examples of background nutrients in wastewater can be found in Table S12 and S13. Energy consumption Energy consumption during cultivation stage mainly consisted of: 1) pumping water in/out of the RW/PBR, 2) pumping algae slurry to harvesting stage and recycling water back to RW/PBR, 3) mixing algae culture in RW/PBR and 4) carbonation (CO2 injection). The energy consumption for pumping water can be calculated using Eq. S7 and S8 (water delivery equations) by omitting the pipeline friction and minor loss terms. The values reported by the exiting studies (0.009-0.36, mean=0.092 kWh/m3) and a fitted lognormal distribution are summarized in Table S14. The mixing of algae culture in RW is achieved by paddle wheels. A typical paddling speed for

12


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452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479


daytime operation (10-16, mean=13 hr/d) is 0.2-0.3 m/s (mode=0.25) and during night the speed is reduced (e.g. to 0.2 m/s)38-40 to save energy. The power required for paddling can be determined by Eq. S14-S16 which explicitly considers both friction and kinetic head losses.41,42 Another more generic equation proposed by Murphy and Allen34 estimates the paddling power in the unit of “W/m2-surface area” (Eq. S17). The power required for paddling for a given RW can be scaled based on its surface area, using this equation. A scenario analysis (Table S15) shows that the total power required for a RW differs significantly between the two equations. Eq. S1416 are recommended when RW design specifications are available, as it allows a higher resolution in specifying head loss. The energy for carbonation is typically not accounted for separately in the literature, but is included in the aggregated results, such as “pond mixing” or “cultivation energy”. The energy for carbonation in RW can also be calculated by Eq. S14. The head loss caused by the carbonation sump can be determined via Eq. S18 and S19 (Stephenson 2010). It should be noted that these equations require extra design information, such as average bubble rise velocity and dimension of the sump. This level of details is usually not available for most LCA studies. In that case, sump depth (e.g. 3 m) may be used as a proximate estimation for head loss. This approximation neglects the “gas hold-up” effect caused by a combination of factors, including bubble size distribution in CO2 stream, cross-section area of the sump, etc. Carbonation and culture mixing are achieved simultaneously via CO2 streaming injection in PBR and the combined energy consumption can be calculated by Eq. S2022 In literature, few studies reported energy required for PBR (4 of 18 studies) and the values vary significantly among different types of PBR (536,760 W/m3; Table S14). As the reported data is sporadic, neither a single value (e.g. mean of the reported range) nor a fitted distribution is recommended for estimating the energy consumption for carbonation and culture mixing in PBR. Instead, estimation should be based on Eq. S20 by using the PBR design specifications. Table H1. Harmonized inventory dataset for upstream and algae cultivation stages Nominal dataset CO2 procurement and delivery Pure CO2 from MEA 0.36 kWh/kg sorption of CO2 flue gas Virgin CO2 1.15 kWh/kg from NH3 CO2 production Virgin CO2 0.07 kWh/kg from EtOH CO2 production Untreated flue gas

CO2 concentration = 15%

Assumptions

Uncertainty dataset Triangular distribution: min=0.23, max=1.85, mode=0.36 Lognormal distribution: µ=1.15, σ=0.17

Mode of uncertainty range Mean of uncertainty range Mean of uncertainty range

Uniform distribution: 0.04-0.10

Mode of uncertainty range

Triangular distribution: min=8%, max=20%,

13


Assumptions/References

[18,26,30]

[13,28,30]

[43,44]

[6]


Page 14 of 31

mode=15%

CO2 delivery from source to algae farm

CO2 input

CO2 injection to culture (carbonation) Water delivery from source to algae farm Water injection to culture Pumping algae slurry between cultivation and harvesting Algae culture mixing

Screeninglevel: 0.02 kWh/kg CO2 Detailed modeling: CP or LPPT model Theoretical CO2 demand: Screeninglevel: 1.9 g/g algae Detailed modeling: stoichiometry of algae CO2 utilization rate=90% RW: Eq. S14, S18-19 (optional) PBR: Eq. S20

Screening-level data from Kadam et al.26

Triangular distribution for CO2 utilization rate: min=50%, max=95%, mode=90%


[2,6,13,23,27,35,37,40,41,4453]

RW: sump depth may be used as a proximate estimation for head loss for Eq. S13

Eq. S7-S8

RW: Eq. S14-16/Eq. S17 PBR: combined with carbonation Screeninglevel: 25 g/m2/d

Algae productivity in RW

State-level, low estimate: Eq. S9-13

Grown in RW with sufficient nutrient supply

State-level, high estimate: Table S11 Algae composition

Table S37

Grown in RW with sufficient nutrient supply Mean and standard deviation from reviewed LCA studies

14


Page 15 of 31


N: urea; P: DAP

Nutrient input

Theoretical demand: stoichiometry of algae

Uniform distribution for nutrient utilization rate: 75%90%

Mean of uncertainty range

[23,35-37]

Nutrient utilization rate=82.5% GHG emission: EPA eGrid datab Electricitya

480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505

a

Water consumption: EIA Form 923 Schedules 821

Data for all 50 states

Same for all stages https://www.epa.gov/energy/emissions-generation-resource-integrated-database-egrid

b

Harvesting As reported from the reviewed studies, harvesting typically accounted for 10% to 35% of the total energy input, with a few outliers, such as 1-4%2,30,53 and over 90%.54 Upon harvesting, the algae biomass concentration is typically 0.05% (0.02-0.15%). The concentration after flocculation does not differ significantly between auto(bio)-flocculation (0.1-5, mode=1%) and flocculant-added process (0.8-3, mode=2%). The typical dose range for chitosan, PH lime and Al2(SO4)3 are 30-150, 300, and 70-100 g/m3, respectively. The cell recovery efficiency after decanting (e.g. via a traveling bridge) is typically 90%. Dissolved-air flotation (DAF) tends to increase the algae concentration to 10% (1.51-10%). The final concentration after filtration (2025%, mode=20%) or centrifugation (10-30, mode=20%) are similar. The reported values for harvesting efficiencies are summarized in Table S16. The un-harvested algae are assumed to be returned to the cultivation stage with the recycled growth media. The energy for mixing during flocculation is often neglected and the reported values are sporadic (Table S17), which is due to different choices of pump, mixing speed, etc. Therefore, to reduce the uncertainty, the energy consumption should be calculated using head loss and pumping efficiency that are specific to the flocculation tank. Similarly, the reported energy values for decanting after flocculation is limited and inconsistent. As decanting is expected to be an intermittent operation, its energy consumption is likely to be negligible as compared to other operations. The reported energy consumption values for DAF, filtration and centrifuge are converted to the unit of “kWh/m3” (Table S17). Table H2. Harmonized inventory dataset for harvesting stage Nominal dataset

Assumptions

Uncertainty dataset

15




Harvesting Table S16 efficiencies 1st dewatering

Flocculant input (g/m3)

Mode values

Table S16

Max and min values for uniform distribution

Uniform distribution: Chitosan: 30-150

Chitosan:90 Al2(SO4)3: 85

[5,23,27,53-57]

Al2(SO4)3: 70-100

Flocculant Eq. mixing S14/S17 nd 2 dewatering Pumping algae slurry for 2nd 0.36 dewatering 3 (kWh/m slurry) DAF (kWh/m3)

Press/filtration (kWh/m3)

Centrifuge (kWh/m3)

506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521

Page 16 of 31

[6]

6.8E-3


0.52


4


Uniform distribution: min=1.8E-3, max= 1.07E2 Uniform distribution: min=0.20, max=0.88 Triangular distribution: min=0.4 , max=9.9, mode=4

[27,48, 52,59,64]

[5-7,37,40,47,51]

[2,5-7,22,27,36,41,46,48,49,53,58,59]

Extraction As reported from the reviewed studies, extraction stage accounts for 3% to 85% of the total energy input for algae biodiesel production, with most results in the range of 13% to 48%. Conventional solvent extraction A total of 47 studies mentioned the investigation of conventional solvent extraction: 45 hexane extraction (dry and wet combined) and 2 other solvents (DME and chloroform-MeOH). Typically, a cell disruption step is required prior to the extraction to release the lipid content. The most commonly used technology is pressurized homogenization and the energy consumption ranges from (0.14-0.27 kWh/kg algae-processed, Table S18). The dry extraction process typically requires the moisture of the algae slurry to be 10% or less. The theoretical energy requirement for evaporating 1 kg water (from 20 °C to 100 °C) is approximately 2.6 MJ. The dry extraction process has been well studied and the ranges of energy consumption and extraction yield have been summarized by Sills et al.6 In order to avoid the energy consumption for drying, wet extraction processes have been proposed by many studies.27,38,44 The reported values and the

16


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522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560


fitted distribution for wet extraction are summarized in Table S19. It is shown that a high solvent-to-algae ratio is needed in order for the wet extraction process to achieve a comparable yield with the dry extraction. Consequently, additional energy is consumed during the solvent recovery step, which may offset the energy saving from avoiding the drying process. Sc. CO2 extraction The energy consumption for Sc.CO2 extraction is reported in a wide range (2.3-30.9, mean=12.9 MJ/kg oil-extracted; Table S20), due to various assumptions on operational conditions. Here, the Sc.CO2 extraction process is modeled in Aspen Plus V9 using in-house experimental conditions (2,000 psig, 40 °C; section S5). The majority of energy consumption is for compression of recycled CO2 from the lipid-CO2 separation step. By assuming an 80% heat exchanger efficiency, the net electricity consumption is 1.9 MJ/kg oil-extracted with a 97% extraction yield. This result is considerably lower than the reported values, because water was assumed to be removed prior to extraction. Hydrothermal liquefaction (HTL) A wide range of biocrude yield has been reported, due to differences in algae composition and HTL operation condition.60-62 The component additivity model proposed by Leow et al.62 using lipid%, protein% and carbohydrates% as independent variables, is recommended for the estimation of biocrude yield (Eq. S21). This model requires only the algae composition data while providing a good estimation of biocrude yield for HTL at 300 °C62. The aqueous phase from HTL can be utilized to generate an energy feed via digestion or gasification. Likewise, the gaseous phase can be utilized to generate energy via combustion. Data from multiple references (Table S24) were reviewed, however, due to the lack of fit, no component additivity model could be generated for the yield of aqueous, gas or solid phase. Alternatively, the yield of aqueous phase was determined based on the biocrude yield (Figure S5). Similarly, the yield of gas phase was determined based on the aqueous phase (Figure S6). The yield of solids was assumed to be the difference between mass of the harvested algae and the mass of other co-products mentioned above. Using the harmonized algae composition (Table S37) and regression equations (Figure S5 and S6), the calculated products yield for generic algae, Chlorella sp., Nannochloropsis sp., and Scenedesmus sp. are summarized in Table S25. To minimize the uncertainty, a first principle calculation is recommended for estimating energy consumption for HTL. During the HTL process, the dewatered algae are heated from 20 °C to 300 °C. The specific heat and heat of evaporation of water are used as the proxy, considering the low algae concentration (mode = 20 %TS). The estimated heat demand for HTL process is 1.3 MJ/kg algae treated. Table H3. Harmonized inventory dataset for extraction stage Nominal dataset

Assumptions

17

Uncertainty dataset




Drying

2.6 MJ/kg water

Heat from 20 to 100 °C to evaporate water

Heat efficiency: 5085%

Cell disruption

0.2 kWh/kg algae processed


Uniform distribution: min=0.14, max=0.27

Extraction yield

95%

For a fair comparison, use mode of the reported values for dry, wet and Sc.CO2 extraction

Page 18 of 31

[63,64] TS% range: 14-30% [27,30,31,48,53]

Hexane extraction (dry) Hexane consumption

0.06 kg/kg oil extracted

Electricity

0.1 kWh/ kg oil extracted

Heat

3.3 MJ/ kg oil extracted


Lognormal distribution: µ=0.06, σ=1.2 Lognormal distribution: µ=0.1, σ=1.2 Lognormal distribution: µ=3.3, σ=1.2

[6]

Hexane extraction (wet) Hexane consumption

0.06 kg/kg oil extracted

Electricity

0.5 kWh/ kg oil extracted

Heat

9.3 MJ/ kg oil extracted

Assumed to be the same as dry extraction


Lognormal distribution: µ=0.06, σ=1.2 Weibull distribution: λ=0.6, κ=1.02 Normal distribution: µ=9.3, σ=5.3 (truncate at zero)

[6]

[27,30,31,35,38,44,58]

Sc. CO2 extraction Dry

1.1 kWh/kg oil extracted Mean of uncertainty range

Wet HTL Biocrude yield Process energy input

561 562 563 564 565 566 567 568

7.9 kWh/kg oil extracted

Uniform distribution: min=0.53, max=1.85 Uniform distribution: min=7.2, max=8.6

[71] and Simulation by this study

[66,67]

Table S25 1.3 MJ/kg treated

Heating water as proxy

Heat efficiency: 5085%

[63,64]

Upgrading Upgrading stage typically accounts for a small portion of the total energy input. The reported results for algae biodiesel production ranged from 1% to 27%, with most results centered around 15%. Biodiesel The conventional approach for biodiesel production has been well-established. Sills et al.6 performed a comprehensive summary of the material and energy consumptions for the acid

18


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569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605


pretreatment and alkaline transesterification steps, which is considered as a representative data set for this study. Several studies have investigated direct transesterification of dry algae, although the associated LCA studies are limited.5,68-74 In general, the reaction time and temperature of direct transesterification is within the range of conventional transesterification, while the energy consumption for MeOH recovery is expected to be significantly higher, considering the excessive MeOH-to-algae (vol/wt) input ratio (Table S26). Therefore, reaction energy consumption is assumed to be the same as conventional transesterification, while the energy consumption for MeOH recovery step is modeled in Aspen Plus (section S6). Three MeOH-toalgae input ratios, 3:1, 10:1 and 20:1, are modeled and the corresponding energy consumptions (with 80% heat recovery efficiency) are 3.6, 10.5 and 8.0 MJ/kg biodiesel, respectively (Table S27). It is noteworthy that energy consumption does not monotonically increase with the concentration of excessive MeOH; therefore, scaling the energy consumption based on MeOHto-algae input ratio may lead to over/underestimation. This may be explained by the tradeoff between reduced vacuum demand (for separation) and increased mass of MeOH processed, when MeOH concentration is increased. To avoid saponification, the acid catalyst (e.g. H2SO4) is often used in direct transesterification and the catalyst input ratio is considerably higher than conventional transesterification. Hydroprocessing Huo et al.75 and Sills et al.6 are the two most frequently cited data sources for energy consumption, material use and coproducts generation for hydroprocessing. Huo et al.75 modeled two production routes in Aspen. The total energy consumption for the two types of renewable diesel are similar while each production route has its unique co-production generation. Therefore, it is recommended that the type of RD should be clearly stated when conducting a LCA for the relevant purpose. Alternatively, Sills et al.6 modeled the occurrence of three underlying reactions of hydroprocessing (hydroxydeoxygenation, decarboxylation and decarbonylation) via a stochastic approach, and estimated the material input (e.g. H2), product yield and energy consumption based on regression models. The details of these regression models are summarized in section S3.3.3.2 of Sills et al. (2012). The range of energy consumption calculated based on the information from Sills et al. (Table S29) overlaps with the values reported by Huo et al.75 As Sills et al.6 did not specify the type of RD studied, the calculated results in Table S29 are recommended for RD production in general. On the other hand, the data from Huo et al.75 is recommended when a particular type of RD is of interest. Table H4. Harmonized inventory dataset for fuel upgrading stage Nominal dataset Conventional transesterification MeOH 0.1 (kg/kg consumption fuel)

Assumptions


19

Uncertainty dataset


Lognormal distribution: µ=0.1,

[6]



Page 20 of 31

σ=1.1 Process electricity consumption

0.04 kWh/kg oil

Process heat consumption

0.9 MJ/kg oil

HCl

0.01 kg/kg oil

KOH

0.01 kg/kg oil

H2SO4

0.001 kg/kg oil

Lognormal distribution: µ=0.04, σ=1.1 Lognormal distribution: µ=0.9, σ=1.1 Lognormal distribution: µ=0.01, σ=1.1 Lognormal distribution: µ=0.01, σ=1.1 Lognormal distribution: µ=0.001, σ=1.1

Direct transesterification (dry) 0.1 (kg/kg fuel)

Assumed to be the same conventional transesterification

MeOH input

10:1 (ml/g dry algae)

Medium case

H2SO4 input

0.6 (g/g dry algae)

Average of low and high cases

MeOH consumption

606 607 608 609 610 611 612 613 614 615 616 617 618 619

Process electricity consumption

0.04 kWh/kg oil

Process heat consumption

0.9 MJ/kg oil

Additional energy for MeOH recoverya Hydroprocessing

10.5 MJ/kg biodiesel

Assumed to be the same conventional transesterification

Based on medium MeOH input ratio

Lognormal distribution: µ=0.1, σ=1.1 Low: 3:1 Medium: 10:1 High: 20:1 Low: 0.2 High: 1.1 Lognormal distribution: µ=0.042, σ=1.07 Lognormal distribution: µ=0.9, σ=1.1 Low: 3.6 Medium: 10.5 High: 8.0

[6]

[5,68,73,74]

[6]

Simulation by this study Table S29

a

The process energy consumption (from conventional transesterification) also includes the energy for recovering the excessive MeOH (based on 6:1 molar input ratio). However, as the amount of excessive MeOH is significantly larger in the case of direct transesterification, the doublecounting of energy for MeOH recovery (in process electricity and heat) is considered as negligible.

Defatted biomass treatment and co-products management As anaerobic digestion (AD) is the most frequently investigated technology for defatted biomass treatment in existing studies (26 studies), harmonization is performed for AD only. The inventory data of other less investigated technologies is summarized in Table S30. The reported results for AD account for 4% to 57% of the total energy input of algae biodiesel. Anaerobic digestion The energy consumption for AD operation and biogas generation are largely influenced by the volatile solids (VS) concentration and its destruction rate (fraction of VS destructed). However, this information is neglected by most studies. A 90% VS concentration27,38,49 and a triangular

20


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620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648


distribution (min=18%, max=73%, mode=55%) for VS destruction rate are recommended for the nominal analysis, based on data in Table S31. The electricity and heat consumption data for AD operation are fitted to triangular distributions. The energy consumption for biogas cleanup is from Frank et al.27 Most studies use a single/range of values for CH4 generation. Preferably, the biogas generation rate should be determined based on the composition of the defatted algae which may be considerably different between different species. (Eq. S22). The CH4 fugitive loss during the digestion and biogas cleanup steps is assumed to be 2%.2, 27 Similarly, the amount of N released (as NH3) during digestion should be determined based on the composition of defatted algae as well. The fraction of N released to digestion liquid ranges from 60 to 80%.13,27,30,31,35,41,47 A 5%27 loss rate due to NH3 volatilization was assumed when digestion liquid is recycled for algae cultivation. Several studies considered the bioavailability of N when digestion solids are applied as the displacement of synthetic fertilizers.27,30,31 The range is 2040%27,30,31 and 1% of N is assumed to be lost as N2O.27,38 The CO2 in biogas and generated from the combustion of CH4 are recycled back to cultivation stage to offset the demand for external CO2 supply. The sequestration rate for carbon in digestion solids is assumed to be 0-16%.27 Co-products management The types of co-products from algae biofuel production depend upon the choices of technology for extraction, fuel upgrading and defatted biomass treatment. For example, the co-products from algae biodiesel production, coupled with AD of defatted biomass, are typically glycerin from transesterification process, and electricity and heat generated from combustion of biogas generated from AD. The yield of glycerin is typically estimated based on the stoichiometric ratio to biodiesel yield. Sills et al.6 provides a good estimation of electricity and heat generation efficiencies from combined heat & power (CHP) generation. If no further treatment is applied to defatted biomass, it is typically assumed to be used as animal feed additive to displace soy meal based on equivalent protein content. For renewable diesel, the co-products are typically a mixture of heavy oil, gasoline and propane from hydroprocessing.6,75 Table H5. Harmonized inventory dataset for defatted algae treatment stage and co-products Nominal dataset

Assumptions

Uncertainty dataset


AD VS concentration

VS destroy rate

Process electricity consumption Process heat

90%

55%

0.08 kWh/kg TS

Most frequently reported value Mode of the uncertain range

Mode of the uncertain range

2.4 MJ/kg TS

21

Triangular distribution: min=18%, max=73%, mode=55% Triangular distribution: min=0.05, max=0.22, mode=0.08 Triangular


[6]

[6,51]


consumption

distribution : min=0.4, max=2.7, mode=2.4

Biogas clean up

0.4 kWh/kg CH4

Digestion solids dewatering

See 2nd dewatering in harvesting stage

CH4 generation rate (L/g VS)

Eq. S22

CH4 fugitive loss N release to digestion liquid N loss as NH3 from digestion liquid N loss as N2O N bioavailbility as soil fertilizer from digestion solids

2%

70%

5%

Only data source available27 Assume same technology for algae 2nd dewatering is used for digestion solids dewatering Based on defatted algae composition20 Most frequently reported value Mean of the uncertain range

Uniform distribution: min=60%, max=80%

1%

[27,38] Mean of the uncertain range

Carbon sequestration in soil

8%

Mean of the uncertain range

CHP electricity efficiency

33%

Mode of the uncertainty range

CHP overall efficiency

76%

Uniform distribution: min=20%, max=40% Uniform distribution: min=0%, max=16% Triangular distribution: min=28%, max=38%, mode=33%

Co-products (most common assumptions)

Defatted algae (untreated) from solvent extraction Propane mix from hydroprocessing Electricity and heat from biogas (AD)

1:1 mass ratio to displace petroleum-based glycerin 1:1 proteinequivalent ratio to displace soy meal 1:1 energyequivalent ratio to displace propane 1:1 energyequivalent ratio

[30,31,35]

[6]

Most frequently reported value

30%

Glycerin from transesterification

Page 22 of 31

Equivalent functionality

22


[23,30,31]

[27]

[6]

Page 23 of 31


to displace electricity from grid and heat from burning natural gas

23



649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688

Mass balance Although several studies have discussed the mass balance for water and nitrogen, a well-defined procedure is missing for explicitly calculating the mass flows throughout the entire life cycle. This section provides the illustration of such a procedure for both water and nitrogen balances. Water balance Figure S8 shows an example of direct water inputs and outputs for a cradle-to-gate algae biodiesel life cycle (see details in Section S8). Lake evaporation models are most frequently used for estimating state-level evaporation loss and the corresponding data for 48 continental US states from Murphy and Allen34 are recommended. The reported blowdown-to-evaporation ratio ranges from 11 to 100%, which is influenced by both climate condition (evaporation rate) and growth media. A commonly used leak rate range is 1.1-3.6 L/m2/d.34,76,77 Typically, the RW pond is emptied 2-4 times a year for clean out and maintenance. Water evaporation also occurs when drying is applied for dry extraction and/or digestion solids dewatering. The total direct water consumption calculated from the example (Figure S8) is 1.1 m3/kg (0.03 m3/MJ) biodiesel, which is smaller than that of petroleum diesel (0.1 m3/MJ).78 This result is also smaller than onethird of the reported results. Nonetheless, a full water consumption analysis should also include the indirect water consumption which is mainly the process water consumption for electricity generation and material manufacturing (e.g. synthetic fertilizers). Table S36 summarizes the consumptive water use for electricity generation at state level for the US.21 Similar to GHG emissions (EPA eGrid, Table S36), water consumption for electricity generation varies considerably among states, indicating the importance of using state-level information for a more accurate estimation. N balance The N balance is affected by volatilization rate, N utilization rate, type of growth media (background concentration), and downstream processing technologies (e.g. solvent extraction vs HTL, digestion vs combustion). This example (Figure S9) assumes freshwater growth and the same algae biodiesel life cycle (as for water balance calculation). The N inputs and outputs are shown in Figure S9 and the result indicates that recycling digestion liquid for algae cultivation can reduce 57% of makeup N input when 70% of N is released during AD. The reduction is most sensitive to N release rate. For example, the reduction becomes 40% and 73% when N release rate is 50% and 90%, respectively. The details of the calculation procedure are in section S9. 3.5 Evaluating inherent uncertainties of algae biofuel process trains using harmonized database The harmonized database standardizes the assumptions, data source and calculation procedures for each stage of the algae biofuels. By reducing the “noises” (e.g. different assumptions for algae growth rate modeling), it is feasible to identify the environmentally-preferred process trains for algae biofuels, based on the “true” (inherent) uncertainties of the technology options

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689 690 691 692 693 694 695 696 697 698 699 700 701


(e.g. difference in the range of energy consumption between dry and wet lipid extraction technologies). Meanwhile, an Excel® Macro-based tool, named “Meta-Analysis Tool for Algae Biofuels (MATAB)”, has been created to streamline the comparison of environmental impacts for different algae biofuel process trains, using the harmonized database created by this study. The effectiveness of harmonization, i.e., reduction in the range of impact results (compared to the range of reported values in the literature), will be investigated by using the MATAB tool in a future study. Supporting information Detailed model and database documentation. This material is available free of charge via the Internet at http://pubs.acs.org.

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Meta-analysis and Harmonization of Life Cycle Assessment Studies

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