Biomass Characterization and Valorization (NREL)

The goal of NREL research as part of the DISCOVR consortium is to develop technologies to both characterize and valorize algal biomass composition for novel species identified and deployed. In particular by studying the dynamics of biomass composition based on physiological and environmental inputs [3,5] , we will be in a position to tailor the quality of biomass materials supplied to maximize the output from a conversion process, through in depth manipulation of strain and cultivation characteristics. The conceptual biorefinery is used to ultimately define the biomass value and calculate the fuel yields and prices [7].

The NREL Biomass Compositional Analysis group has developed and maintains a set of reference analytical procedures that are applied to objectively compare the respective biomass composition of novel species of algae identified and studied under the DISCOVR framework [6,12]. The methodology of primary biomass compositional analysis has been applied to biomass obtained from both controlled cultivation conditions in the PNNL LEAPS reactors as well as from the outdoor cultivation of some of the more promising species in the context of the State of Technology demonstration runs [1,2,8,9]

Furthermore, in the context of extraction of valuable bioproducts from an algae-to-biofuels and products conversion pathway or biorefinery, the NREL team is also applying a small-scale mimic of a conversion process (Combined Algal Processing or CAP[10, 11]) to biomass collected from the selected DISCOVR species. This will allow for the study of both the respective sample susceptibility to pretreatment and release of biofuel-relevant lipids and carbohydrates, and simultaneously indicate whether these conversion routes are compatible with a downstream pathway.

The methodology for compositional analysis of algae follows a dedicated set of procedures that have been developed and maintained at NREL since 2013. These procedures are freely available online and routinely used throughout the algae community. The basis of compositional analysis work is that the procedures are set up to deliver biomass composition data by respective component quantification. The overarching approach guides the integration of the individual procedures to measure algal biomass constituents in an unambiguous manner and ultimately achieve mass balance closure for algal biomass samples. By combining the appropriate procedures, the goal is to break the biomass sample into constituents that, when quantified, sum to as close to 100% by weight as possible. Some of these constituents are individual components, such as carbohydrates and lipids as total fatty acids, and some can be groups of compounds, such as extractable lipids. The respective composition in response to varying environmental conditions can inform a downselection algorithm designed to prioritize species and strains of algae.

The process to test the susceptibility to conversion to fuels and products, sometimes referred to as ‘reactivity’ of the biomass, is based on a response surface analysis of a small-scale pretreatment system in a microwave reactor. Aliquots of biomass from different species and growth conditions are subjected to a 7 mL scale pretreatment with sulfuric acid followed by quantification of monomeric sugars in the liquor and extractable lipids based on a batch hexane solvent extraction. The response of extractable lipids and solubilized carbohydrates is measured in the different fractions of a mimicked conversion reactor and the respective kinetics of response to increasing acid or temperature of pretreatment provides us with a proxy of how the biomass can be converted to biofuel precursors and thus informs a downselection algorithm.

Initial work has characterized the baseline biomass composition for at least 10 different species selected for downselection. Biomass was collected when the species were grown under nutrient replete and deplete environments, to allow for the study of the compositional rearrangement dynamics. The data shown in Figure 1 illustrate the very different picture of biomass composition for the different samples analyzed that is driven by both the species and genus of algae as well as environmental conditions. The NREL team is currently developing a tool to estimate the respective biomass value based on the components measured and their respective quantitative contribution to the biorefinery model of biofuel and bioproduct synthesis. This approach will allow for an objective comparison of different species and ranking based on their respective metabolic and biochemical capacity to accumulate bioenergy-relevant products.

Figure 1: Overview of primary biomass composition of a subset of species studied under DISCOVR, grown in uniquely designed DISCOVR media, and harvested under nutrient replete (R) or deplete (D) conditions respectively.

The NREL team has characterized the response of four different species/strains of algae to conversion based on a reactivity response surface analysis of a mimicked pretreatment process. The results are shown in figure 2, where the shape of the response surface is an indicator for reactivity of the biomass to extraction effectiveness. The color scheme shows that there are either narrow conversion optima, e.g. for the Monoraphidium species, or broader conditions, e.g. for Scenedesmus. The Chlorella response indicates that for the conditions tested, the recovery of lipids far underperforms compared to other species and suggests other biomass characteristics may interfere with either the hydrolysis or extraction process making the resulting biomass less accessible. The data illustrate differences between species that, if extrapolated to a large scale conversion process, may make or break the economical and technical feasibility.

Benefits

In depth characterization of algal biomass not only provides a direct route to predicting fuel yield but also gives insight into the potential for extracting bioproducts from the same biomass under process-relevant conditions. The NREL process small-scale conversion mimic study allows for rapid testing novel species on whether the fractionation Combined Algal Processing pathway can be applied successfully and allows for maximum valorization of the biomass components.

Contact

Lieve M. Laurens | Lieve.Laurens@nrel.gov

https://www.nrel.gov/research/lieve-laurens.html

https://www.nrel.gov/bioenergy/algal-biofuels.html

https://www.nrel.gov/bioenergy/microalgae-analysis.html

References

1. Knoshaug, E. P., Wolfrum, E. J., Laurens, L. ML., Harmon, V., Dempster, T. A., Crowe, B., and McGowen, J. (2017) “Open Pond Algal Cultivation Datasets of the Algae Testbed Public-Private Partnership: The Unified Field Studies as the Benchmark for Innovative Algae Agronomics”, Nature Scientific Data, 5, Article number 180267
2. Davis, R., Coleman, A., Wigmosta, M., Markham, J., Kinchin, C., Zhu, Y., Jones, S., Han, J., Canter, C., Li, Q. (2017) 2017 Algae Harmonization Study: Evaluating the Potential for Future Algal Biofuel Costs, Sustainability, and Resource Assessment from Harmonized Modeling, NREL Technical report, NREL/TP-5100-70715, https://www.nrel.gov/docs/fy18osti/70715.pdf
3. Arora, N., Laurens, L. M.L., Sweeney, N., Pruthi, V., Poluri, K. M., Pienkos, P. T. (2018) “Elucidating the unique physiological responses of halotolerant Scenedesmus sp. cultivated in sea water for biofuel production” Algal Research, 34, 260-268
4. Leow, S., Shoener, B. D., Debellis, J. L., Markham, J., Davis, R., Laurens, L. ML., Pienkos, P. T., Cook, S. M., Strathmann, T. J., Guest, J. S. (2017) “A Unified Modeling Framework to Advance Biofuel Production from Microalgae”, Environmental Science & Technology, 2018, 52 (22), 13591–13599
5. McKie-Krisberg⁠, Z. M., Laurens⁠, L. M.L., Huang⁠, A., Polle, J.W. (2018) “Comparative energetics of carbon storage molecules in green algae”, Algal Research, 31, 326-333
6. Palardy, O., Behnke, C., Laurens, L. ML.* (2017) “Fatty Amide Diversity in Neutral Molecular Fractions of Green Crude Hydrothermal Liquefaction Oils from Algal Biomass” Energy and Fuels, 31 (8), 8275-8282, doi: 10.1021/acs.energyfuels.7b01175
7. Laurens L. ML., Markham, J., Templeton, D. W., Christensen, E. D., Van Wychen, S., Vadelius, E., Dong, T., Chen-Glasser, M., Davis, R., Pienkos, P. T. (2017) “Development of Algae Biorefinery Concepts for Biofuels and Bioproducts; a Perspective on the Molecular Identification of Process-compatible Bioproducts and Impact on Cost-Reduction” Energy and Environmental Science, 10, 1716—1738, doi: 10.1039/C7EE01306J
8. McGowen, J., Knoshaug, E. P., Laurens, L. ML., Dempster, T. A., Pienkos, P. T., Wolfrum, E., Harmon, V. L. “The Algae Testbed Public-Private Partnership (ATP3) Framework; Establishment of a National Network of Testbed Sites to Support Sustainable Algae Production” Algal Research, 25,168-177, doi.org/10.1016/j.algal.2017.05.017
9. Laurens, L. ML., Van Wychen, S., Pienkos, P.T., Harmon, V. L., McGowen, J. “Harmonization of Experimental Approach and Data Collection to Streamline Analysis of Biomass Composition of Algae in an Inter-Laboratory Setting” Algal Research, 25, 549-557, doi:10.1016/j.algal.2017.03.029
10. Dong, T., Knoshaug, E., Davis, R., Laurens, L. ML., Van Wychen, S., Pienkos, P.T., Nagle, N. (2016) “Combined Algal Processing: A novel integrated biorefinery process to produce algal biofuels and bioproducts” Algal Research, 19, 316-323 doi:1016/j.algal.2015.12.021
11. Dong, T., Van Wychen, S., Nagle, N., Pienkos, P.T., Laurens, L. ML.* (2016) “Impact of Biochemical Composition on Susceptibility of Algal Biomass to Acid-Catalyzed Pretreatment for Sugar and Lipid Recovery” Algal Research, 18, 69-77 doi:10.1016/j.algal.2016.06.004
12. Templeton, D. W., Laurens, L. ML. (2015) “Nitrogen-to-protein conversion factors revisited for applications of microalgal biomass conversion to food, feed and fuel” Algal Research 11:359-367 10.1016/j.algal.2015.07.013
13. Laurens, L. ML., Nagle, N., Davis, R., Sweeney, N., Van Wychen, S., Lowell, A., Pienkos, P. T. (2015) "Acid-catalyzed algal biomass pretreatment for integrated lipid and carbohydrate-based biofuels production" Green Chemistry, 17, 1145-1158 doi: dx.dio.org/10.1039/C4GC01612B