The Experimental Case is comprised of five batches, ranging in vol­ume from 970 L to 2000 L each. A marine species of Chlorella (KAS 603, provided by Kuehnle AgroSystems, Inc.) was used for all batches and was grown in four different growth stages: flasks, airlift photobioreactors, greenhouse tanks, and covered raceway ponds.

This growth process provided a stable method for scaling up cultiva­tion volumes, although, the inherent inefficiencies of operating at lab — scale required high energy and material inputs (an artifact described in detail by Beal et al. [14]) and yielded relatively low biomass and lipid productivities, as listed below. Energy and material consumption were measured throughout the entire cultivation process and these data have been reported previously [14]. The amounts of resources consumed in the smaller growth volumes (e. g., energy required for bioreactor lighting) were allocated to the larger growth volumes as the algae were transferred through the system during scale-up (cf. Appendix 4A of [19] for details).

— — — — — Highly Productive Case EROI Highly Productive Case QA EROI and FROI

• — • — Experimental Case EROI

——————- Experimental Case QA EROI and FROI *

The algal biomass was tracked during each batch by measuring the dry cell weight of multiple samples collected throughout the production pathway. These samples were centrifuged and the pellet was rinsed three times to remove salts. Then, the samples were maintained at 70 °C until a constant weight was obtained. High performance liquid chromatography (HPLC) was used to calculate the lipid content and lipid composition for each batch according to methods developed at The University of Texas at Austin [21], which are refinements of standard methods [22-24].

All five of the experimental batches were processed using a centrifuge for harvesting, electromechanical cell lysing, and a microporous hollow — fiber membrane contactor for separations. While the energy and materials consumed during each of these steps, and the associated uncertainty, has been described in detail by Beal et al. [14,19], this study, combines these data with monetary costs, water impacts, and resource constraints for each input.