Generation of Biomass Productivity Maps for Conducting Techno-Economic and Lifecycle Analyses

High productivity strains that performed well during TIER V testing in outdoor SOT testbed at AzCATI will be experimentally characterized to provide the required model input parameters for running the PNNL biomass growth model (Huesemann et al., 2016) in conjunction with the PNNL Biomass Assessment Tool (Wigmosta et al., 2011). Biomass growth modeling results will be used to generate biomass productivity maps (see Figure 1 as example) for the best performing strains, and to evaluate strategies to increase annual biomass productivities via crop rotation and pond operational strategies (i.e., choice of pond depth, dilution rate, harvesting times). These data will also serve as important input to ongoing techno-economic and life-cycle analyses (Davis et al., 2016).

Biomass productivity map

Figure 1.  Model-predicted mean annual biomass productivity map for Chlorella sorokiniana (DOE 1412) (Source: Supplemental Figure S2 in Huesemann et al., 2018). The highest predicted annual biomass productivity was found to occur in Key West, Florida. The PNNL microalgae biomass growth model (Huesemann et al. 2016) was coupled with the PNNL Biomass Assessment Tool (Wigmosta et al., 2011) to estimate the 30-year annual mean biomass productivity of Chlorella sorokiniana (DOE 1412) for a total of 2522 open pond locations distributed across the United States. The simulated batch cultures (20.5 cm depth) were repeatedly started at a biomass concentration (AFDW) of 0.05 g L-1 and harvested at 0.5 g L-1. Model inputs consisted of (a) species-specific biomass growth rate, dark respiration, and light attenuation parameters for Chlorella sorokiniana (Huesemann et al., 2016), (b) hourly meteorological data from the U.S. Department of Agriculture Cligen stochastic weather generator, and (c) hourly open pond water temperature simulated by the Modular Aquatic Simulation System 2-D (MASS2).


Davis, R., J. Markham, C. Kinchin, N. Grundl, E. Tan, and D. Humbird, Process Design and Economics for the Production of Algal Biomass: Algal Biomass Production in Open Pond Systems and Processing Through Dewatering for Downstream Conversion, NREL Technical Report NREL/TP-5100-64772, February 2016.

Huesemann, M.H., B. Crowe, P. Waller, A. Chavis, S. Hobbs, S. Edmundson, and M. Wigmosta, “A validated model to predict microalgae growth in outdoor pond cultures subjected to fluctuating light intensities and water temperatures”, Algal Research, 13:195-206, 2016.

Huesemann, M., A. Chavis, S. Edmundson, D. Rye and M. Wigmosta, “Climate-Simulated Pond Culturing: Quantifying the Maximum Achievable Annual Biomass Productivity of Chlorella sorokiniana (DOE 1412) in the United States”, Journal of Applied Phycology, 30:287-298, 2018.

Wigmosta, M.S., A.M. Coleman, R.J. Skaggs, M.H. Huesemann, and L.J. Lane, “National microalgae biofuels production potential and resource demand”, Water Resources Research, 47:2011.


Michael Huesemann

Pacific Northwest National Laboratory, Marine Sciences Laboratory