Combinatorial life cycle assessment to inform process design of industrial production of algal biodiesel

Transcription

1 Supporting Information for: Combinatorial life cycle assessment to inform process design of industrial production of algal biodiesel Laura B. Brentner, Matthew J. Eckelman, Julie B. Zimmerman This Supporting Information contains: Equations used for calculating cumulative energy demand at each process stage; Additional text to accompany the main document, exploring additional design considerations associated with wet co-solvent lipid extraction, heating and cooling options, geographic location, use of artificial light, nutrient options, and end-of-life options for the algae cake; Text explaining the results of the sensitivity analysis; Text explaining the results of the uncertainty analysis; Figure S1. Sensitivity of best case LCA to algal culture density, algal oil content, nutrient loading, chitosan dosage, and overall inputs to the conversion process, based on a +/- 10% change in these parameters; Table S1. Summary statistics of Monte Carlo simulation for best case process configuration (1,000 runs); and References for the Supporting Information. 1

2 Equations The equations shown below were used to calculate the cumulative energy demand (CED) of each process in the algal biodiesel production chain. These equations can be applied to other environmental impact categories by substituting the energy use factors in each variable with appropriate emissions or characterization factors from a published impact assessment method. The equations all take the general form: ( ) ( ( ) ) 1 CED= M a CFa Eb 1 CFb ELEC, a b η ELEC where: CED is the cumulative energy demand of the process; M a are the material inputs; E b are the fuel inputs; CF a and CF b are the characterization factors (or primary energy factors) for the production and delivery of materials and fuels, respectively; ELEC is the aggregate electricity use from all subprocesses; and η ELEC is the grid-specific conversion efficiency of primary energy into electricity (including transmission and distribution losses). This can also be expressed as the inverse of the characterization factor for electricity, or CF ELEC -1 The first term of the sum represents the contribution of all process materials to CED, the second represents the direct use of fuels (coal, oil, natural gas) and the upstream energy required to produce and transport these fuels, and the third term represents the contribution of electricity use. Algae Cultivation CED CULT = a, ( M ) a CFa + ELECCULT η ELEC 1 where: the index a runs through the material inputs of carbon dioxide, nutrient inputs, and reactor materials including concrete, steel, and plastics; ELEC CULT includes electricity use from paddle wheel mixing, gas aeration, and pumping water to the cultivation site; and there is no direct use of fuels. Algae Harvesting CED HARV = a, ( M ) a CFa + ELECHARV η ELEC 1 where: the index a runs through the material inputs of filter papers or flocculants; ELEC CULT includes electricity use from centrifugation, applying hydraulic pressure, or mixing, depending on the harvesting process; and there is no direct use of fuels. 2

3 Lipid Extraction and Conversion CED = ( M ) + a CFa ELECEXT _ CONV η ELEC EXT _ CONV, a where: the index a runs through the material inputs of methanol, carbon dioxide, and transesterification catalysts; the index b runs through the fuels used to generate heat for drying, solvent extraction, and transesterification; and ELEC CULT includes electricity use from sonication, running the filter press, pressurization and temperature control of reaction vessels, and/or solvent recovery, depending on the process. 1 Disposal/Reuse of Residual Algal Biomass Contributions to CED from disposal processes are calculated as product of the total mass of material disposed and the characterization factor for the specific disposal process assumed; Credits to CED from reuse of algal biomass for nutrients or energy production from anaerobic digestion are made by substituting for the inputs of nutrients and electricity (via combustion of methane in a gas-fired turbine), as indicated in the main text. 3

4 Additional Algal Process Design Considerations Wet co-solvent lipid extraction Refs 1 and 2 assume a co-solvent lipid extraction using wet algae directly from the harvesting step. While this has been demonstrated on a lab-scale, the potential for creating an emulsion is large when water and lipids are combined and homogenized at the industrial scale, making it difficult to separate out the lipids in scale-up 3, and so this option has not been included in the present study. Heating and cooling options If a heat exchanger were to be included in the flat plate design to facilitate operational temperature control, as described by Sierra et al. 4 the additional energy consumption would increase dramatically. For a five-degree temperature correction, the cumulative energy demand would increase by 8120 MJ, tipping the energy balance of the best case scenario negatively. This increase in energy demand would also be reflected in an increase of GHG emissions of 795 kg CO 2 eq. Larger temperature differences would directly increase the energy requirement. Additional water would be used in the system to run through a heat exchange pipe in the PBR, increasing the cumulative energy demand. This is a substantial consideration that would change the favor of the best algal cultivation method to an ORP, if energy is the main consideration. Pilot scale trials of these different reactor types are limited and results do not span different seasons. 4-7 Location considerations Depending on the location and the value of land area in a given region, limited space for algal cultivation may have a greater influence on the optimal choice of bioreactor and process configuration. Land requirements will vary based on algal yields (see Sensitivity Analysis) which are influenced by climatic parameters such as solar irradiance and temperature. 8 Temperature and humidity will also influence evaporation in open systems, which in turn greatly influence the use of water. Algae may not require arable soils but they will take up land area in previously uncultivated ecosystem. Prior to full-scale expansion of algal biodiesel production, an in-depth ecosystem analysis is recommended to fully understand the impacts to the environment. Artificial light One technological option for algae cultivation that has seen extensive exploration but was not considered here the use of artificial light in addition to or rather than natural sunlight in PBRs allowing for indoor cultivation and thus the siting of algae biodiesel plants in locations without abundant insolation and moderate year-round temperatures. 9 By using artificial light, algal growth can be precisely controlled, leading to higher yields and greater economic viability. These potentially significant benefits have a number of trade-offs, however, particularly for life cycle primary energy use. Indoor cultivation schemes would likely require heating and cooling of some portion the surrounding structure. More significantly, the energy content of biodiesel from microalgae grown in PBRs will be less than the energy required to provide 4

5 artificial light to such systems. Even if the rest of the algae-to-biodiesel system operates with zero losses, the first-law efficiency of algae cultivation in a completely artificial light PBR system would be determined by the efficiencies of primary energy conversion to electricity (~35% in the U.S.), electricity distribution (93%), electricity conversion into photons (10-20%), and photosynthetic in conversion of photons to biomass (6-20%), 10 giving an overall cultivation system efficiency of approximately 1%. Extending this reasoning to greenhouse gas emissions, in the best case configuration, the production of 10 GJ of biodiesel results in emissions of ~800 kg CO 2 e. If artificial light were used as the sole light source instead of sunlight, the rate of biodiesel production would certainly increase with an optimized growing period, but at the expense of at least 10 GJ/1% ~ 1 TJ of additional primary energy used for electricity production, which is equivalent to more than 160 metric tons of CO 2 e of additional GHG emissions, using IPCC 2007 GWP 100 factors and annual output GHG emission rates for the AZNM egrid subregion. This increases the GHG burden of algae biodiesel using the best case scenario by roughly a factor of 200. These results would shift under the assumptions of using electricity only from renewable sources, for example, or using next generation LED bulbs, 11 but only by a factor of 3-6. Thus from a life cycle perspective, artificial light in PBR systems for biodiesel production can only plausibly be used in conjunction with natural light. 12 Nutrient resources The supply of fertilizer to algae will become a major factor in sustainable development of the algal biodiesel industry. On average, by weight algae consist of 6.5% nitrogen and 1% phosphorus (on the basis of the molecular formula for algae, CO 0.48 H 1.83 N 0.11 P ). To supply 50% of the Renewable Fuels Standard for on-road transportation by 2020, 22 billion gallons per year, would take 165 million tons of algae with 50% oil content and a minimum of 10.6 million tons of nitrogen and 1.6 million tons of phosphorus. 14 This would nearly double the current rate of nitrogen consumption by agriculture in the US. 15 Mineral phosphorous resources are limited and not projected to meet these demands. Nitrogen fertilizers are typically synthesized from fossil fuels. Nutrient rich wastewater streams, such as sewage or from confined animal feeding operations have been identified as potential viable sources. Data collected on wastewater treatment in the US estimated 34.4 billion gallons of wastewater are treated each day 16 and with an average of 40 mg L -1 of nitrogen 17 would only amount to around 0.2 million tons per year of nitrogen. While algae can be used to mitigate nitrogen loads in wastewater treatment, an additional reliable source of nutrients is needed to support a commercial algal biodiesel industry. Recycling within the system, as described in the best case scenario is another approach to reduce strain on fertilizer resources. Further research is needed to test the viability of this approach. End-of-life options for algae cake There are other options for algae cake that have been explicitly discussed or implicitly assumed in the literature that are not modeled here. Sander and Murthy 18 discuss fermentation of the carbohydrate fraction to produce ethanol assuming that 30% of the algae is cellulose and the conversion process to ethanol has an 85% mass efficiency, with ethanol production of 6.3 L per 1 GJ of biodiesel produced. Lardon et al. 19 in their accounting of the net energy ratio for algal biodiesel assume that the algae cake will be used for energy production of some kind, and could 5

6 be co-fired with coal to generate electricity and steam. Other options include processing into a 12, 20 replacement for fish meal. 6

7 Sensitivity Analysis Figure S1 shows the results of a 10% sensitivity analysis on the best case scenario for five different parameters: algal culture density, algal oil content, nutrient loading, flocculant dose, and overall inputs for the conversion process. Algal culture density is tied directly to the volume of culture required to produce a set quantity of biomass needed to achieve the oil yield that will be converted into 10 GJ of biodiesel. The total water used, number of reactors needed and total land area occupied will be inversely correlated with algal culture density. Surprisingly little change was observed in the energy demand and global warming potential by a change in culture density, as the energy demand for cultivation only accounts for 3.3% of the base case and 7.2% of the best case. The algal oil content is tied directly to the amount of total algal biomass needed for a set quantity of biodiesel (10 GJ) which has a slightly greater effect on the overall energy demand and global warming potential, decreasing the impacts by 0.7% and 1.7%, respectively, with a 10% increase in algal cell oil content. The quantity of biomass is also set by the efficiency of downstream processes, such as harvesting and direct transesterification so changes in these efficiencies would have a similar effect, although these efficiencies wouldn t be expected to fluctuate as much as 10%. Algal oil content also made a significant impact on eutrophication potential, by around 6% in either direction. A 10% change in nutrient loading changed eutrophication by about 6% in either direction as well. Nutrient loading changes had a more direct, symmetrical effect on the LCA impacts. Nutrient loading affected global warming potential by 1.5% in both directions and cumulative energy demand by 0.6%. Flocculant dose had little effect on the overall impacts. The conversion process, however, is very energy intensive and an increase or decrease in the inputs here are directly related to the environmental impacts of the overall system, except for land use which is not determined by the conversion process. Uncertainty Analysis Uncertainty in the best-case scenario results was determined through Monte Carlo simulation. Nearly all of the variables in the LCA model are functions of the water volume, which in turn is a function of the reactor type and the volumetric productivity of the algae. Volumetric productivity is also one of the most uncertain variables in algal biodiesel LCA models, varying from kg/m 3 for open raceways and 2-8 kg/m 3 for photobioreactors. The uncertainty results for cumulative energy demand show a normal distribution with a mean of 10,800 MJ and a standard deviation of 605, or 5.6% of the mean (Table S1). Monte Carlo results for the other environmental impact categories are more uncertain, particularly for eutrophication, with a standard deviation of nearly 30% of the mean. 7

8 Algal Culture Density Algal Oil Content -12% -8% -4% 0% 4% 8% 12% -12% -8% -4% 0% 4% 8% 12% Cumulative Energy Demand Land Use Water Use Eutrophication Global Warming +10% -10% Nutrient Loading -12% -8% -4% 0% 4% 8% 12% Conversion Inputs -12% -8% -4% 0% 4% 8% 12% Flocculant Dose -12% -8% -4% 0% 4% 8% 12% Figure S1. Sensitivity of best case LCA to algal culture density, algal oil content, nutrient loading, chitosan dosage, and overall inputs to the conversion process, based on a +/- 10% change in these parameters 8

9 Table S1. Summary statistics of Monte Carlo simulation for best case process configuration (1,000 runs). Impact Category Mean Standard Deviation Coefficient of variation Cumulative Energy Demand (MJ) 10, % Global Warming Potential (kg CO 2 e) % Eutrophication (g N eq) % Direct Water Use (m 3 ) % 9

10 References for Supporting Information (1) Stephenson, A. L.; Kazamia, E.; Dennis, J. S.; Howe, C. J.; Scott, S. A.; Smith, A. G., Lifecycle assessment of potential algae biodiesel production in the United Kingdom: a comparison of raceways and air-lift tubular bioreactors. Energy & Fuels 2010, 24, (2) Xu, L.; Wim Brilman, D. W. F.; Withag, J. A. M.; Brem, G.; Kersten, S., Assessment of a dry and a wet route for the production of biofuels from microalgae: Energy balance analysis. Bioresource Technology 2011, 102, (8), (3) Cooney, M.; Young, G.; Nagle, N., Extraction of bio-oils from microalgae. Separation & Purification Reviews 2009, 38, (4) Sierra, E.; Acien Fernandez, F. G.; Garcia, J. L.; Gonzalez, C.; Molina Grima, E., Characterization of a flat plate photobioreactor for the production of microalgae. Chemical Engineering Journal 2008, 138, (5) Chini Zittelli, G.; Rodolfi, L.; Biondi, N.; Tredici, M. R., Productivity and photosynthetic efficiency of outdoor cultures of Tetraselmis suecica in annular columns. Aquaculture 2006, 261, (6) Benemann, J. R.; Oswald, W. J. Systems and economic analysis of microalgae ponds for conversion of CO 2 to biomass; Pittsburgh Energy Technology Center: Pittsburgh, PA, (7) Posten, C., Design principles of photo-bioreactors for cultivation of microalgae. Engineering in Life Sciences 2009, 9, (3), (8) Clarens, A. F.; Resurreccion, E. P.; White, M. A.; Colosi, L. M., Environmental life cycle comparison of algae to other bioenergy feedstocks. Environmental Science & Technology 2010, 44, (5), (9) Ugwu, C. U.; Aoyagi, H.; Uchiyama, H., Photobioreactors for mass cultivation of algae. Bioresource Technology 2008, 99, (10) Janssen, M.; Tramper, J.; Mur, L. R.; Wijffels, R. H., Enclosed outdoor photobioreactors: light regime, photosynthetic efficiency, scale-up, and future prospects. Biotechnology and Bioengineering 2003, 81, (2), (11) Lee, C.-G.; Palsson, B. O., High-density algal photobioreactors using light-emitting diodes. Biotechnology and Bioengineering 1994, 44, (12) Baliga, R.; Powers, S. E., Sustainable Algae Biodiesel Production in Cold Climates. International Journal of Chemical Engineering 2010, Article ID (13) Grobbelaar, J. U., Algal Nutrition. In Handbook of Microalgal Culture: Biotechnology and Applied Phycology, Richmond, A., Ed. Blackwell Science Ltd: Oxford, UK, 2004; pp (14) EPA EPA Finalizes Regulations for National Renewable Fuel Standard Program for 2010 and Beyond; EPA-420-F ; U.S. Environmental Protection Agency Washington DC, (15) USDA Data Sets: Fertilizer Use and Price; U.S. Department of Agriculture: Washington, DC, (16) EPA Clean Watersheds Needs Survey 2008 Report to Congress; U.S. Environmental Protection Agency: Washington, DC, (17) FAO Wastewater characteristics; U.N. Food and Agriculture Organization: Rome, (18) Sander, K.; Murthy, G. S., Life cycle analysis of algae biodiesel. International Journal of Life Cycle Assessment 2010, 15, (19) Lardon, L.; Helias, A.; Sialve, B.; Steyer, J.-P.; Bernard, O., Life-cycle assessment of biodiesel production from microalgae. Environmental Science & Technology 2009, 43, (17), (20) Batan, L.; Quinn, J.; Willson, B.; Bradley, T., Net Energy and Greenhouse Gas Emission Evaluation of Biodiesel Derived from Microalgae. Environmental Science & Technology 2010, 44, (20),