The results of the study are generally in agreement with previous studies highlighting the role of critical processing steps in algae biofuel production—such as the need to develop viable wet lipid extraction technologies, incorporate high-energy coproducts, and reduce energy consumption of algae cultivation. However, the new study extends previous LCA studies with the Monte Carlo approach.

Despite algae’s potential as a renewable energy feedstock, it is not yet clear if algal biofuels can be produced economically in an environmentally sustainable manner. One metric used to measure the economic viability and environmental performance of a fuel is the Energy Return on (Energy) Invested (EROI) ratio, defined as the energy contained in one unit of fuel divided by the total nonrenewable energy required to produce one unit of fuel. Numerous studies have used Life Cycle Assessment (LCA) to estimate EROI and global warming potential (GWP) of algal biofuels. However, Figure 1a [above] shows the high variability of estimated EROI ratios from previous algal biofuel LCA studies, which range from 0.09 to 4.3. This enormous range of predicted values span the “break even” value of 1.0, in which the energy content of produced energy just equals the total nonrenewable energy consumed, demonstrating that there is no agreement as to whether algal fuels are expected to yield net gains in energy.

...While a number of previous LCA studies on algal biofuels examined multiple scenarios and conducted sensitivity analyses to measure the effects of varying single parameters on model outcomes, there were limitations associated with their results. Specifically, these studies presented results as point values, ignoring the combined effects of variability and uncertainties of input parameters. Two studies included uncertainty analyses that simultaneously varied process parameter inputs using Monte Carlo simulations, but the effects of uncertainty on performance of individual unit processes were not presented.

The use of LCA to quantify environmental performance of a fuel assumes that such models produce single value results with minimal uncertainty. This assumption, however, is questionable due to modeling limitations; scenario uncertainty, which results from choices in model boundaries; functional units; coproduct allocation methods; and parameter uncertainty caused by the lack of performance data.

...Our work was motivated by the lack of comprehensive uncertainty analysis in earlier LCA studies. To obtain a better understanding of the expected performance of proposed algae to biofuel processes, LCA models should include an uncertainty analysis that quantifies the effect of simultaneously varying all model parameters on results. Such an improvement would more reliably inform industry and policy makers on expected EROI values and environmental sustainability of algal biofuels.

—Sills et al.

Energy return on energy invested (EROI) for algal biofuel production with low, base, and high productivity parameters, dry and wet lipid extraction process trains. Center lines represent median values, edges of boxes represent 25th and 75th percentiles, and limiting bars represent 5th and 95th percentiles of the distributions resulting from 10,000 Monte Carlo simulations. Credit: ACS, Sills et al. Click to enlarge.

For their study, the Cornell team assumed that marine algae were cultivated at a coastal location in a 1210-ha production facility with access to seawater. The functional unit for the study is 1 MJ of liquid biofuel: biodiesel (fatty acid methyl ester) or renewable diesel (bio-hydrocarbons). The model comprises five unit processes:

• cultivation;
• harvesting and dewatering;
• lipid extraction;
• lipid conversion to a liquid transportation fuel; and
• coproduct production from defatted algae.

Alternative technologies were modeled for each of the five stages. Each process stage was analyzed separately and assembled with the other stages to create six alternative case studies.

A base case analysis, performed to emphasize the importance of the uncertainty analysis, found—as other studies have—that processes with high nonrenewable energy demands were associated with high GWP values (e.g., thermal drying and algal cultivation).

Among their findings:

• Nonrenewable energy demands for algae cultivation ranged from 1.7 to 4.9, 0.94 to 1.8, and 0.7 to 1.3 MJ per MJ biofuel produced for the low, base, and high productivity ranges, respectively.

• The nonrenewable energy demand associated with thermal drying ranged from 1.5 to 2.2 MJ per MJ of biofuel produced.

• Nonrenewable energy requirements for cultivation (modeled with the base productivity), which ranged from 1.1 to 1.7 MJ per MJ of biofuel, may be as large as those for thermal drying. In other words, the team suggested, while wet lipid extraction methods are crucial to yield net gains in energy, decreasing energy requirements for cultivating algae are also needed.

• The distribution of energy demands required for centrifugation is higher than that required for the belt filter press.

• For lipid extraction, hydrothermal liquefaction has lower fossil energy demands than does hexane extraction, and the ranges of energy demand for these two processes do not overlap.

• The range of values for hydrotreatment and transesterification overlap, suggesting that the choice of lipid conversion technology should be based on considerations beyond nonrenewable energy demand.

  Hydrotreatment may have advantages over transesterification, including the use of existing infrastructure and a fuel that is more compatible with existing engines. Too, transesterification produces glycerol as a byproduct, which in previous studies was assumed to be a coproduct; however, large-scale biodiesel production is likely to exceed market demand for glycerol as a coproduct, which will turn this byproduct into a liability.

  Renewable H₂-producing technologies may be developed as part of an integrated biorefinery, which might reduce demands of nonrenewable energy for hydrotreatment.

• The dry extraction route coupled with each of the three productivity ranges resulted in EROI values lower than 1.0, demonstrating that the development of wet lipid extraction processes is crucial to achieve gains in net energy.

• Algae cultivation with low productivity (2.4−16 g·m⁻²·day⁻¹) coupled with dry or wet lipid extraction resulted in EROI values lower than 1.0, showing that it is also crucial to increase aerial productivity of algal feedstocks and lower energy demands associated with cultivation.

• Only high productivity (34−50 g·m⁻²·day⁻¹) combined with wet extraction resulted in EROI values higher than 1.0 over the entire range of results.

The authors noted that the study was limited by the assumption of no correlations among process parameters, and, for some parameters, not accurately knowing their underlying distribution functions used for Monte Carlo sampling.

Resources

• Deborah L. Sills, Vidia Paramita, Michael J. Franke, Michael C. Johnson, Tal M. Akabas, Charles H. Greene, and Jefferson W. Tester (2012) Quantitative Uncertainty Analysis of Life Cycle Assessment for Algal Biofuel Production. Environmental Science & Technology doi: 10.1021/es3029236