Our TEA analysis also revealed that the cyanobacteria cultivation and recovery sections (back-end section) account for 54% of the TCI, 66% of the TOC, 86% of the energy use, and 100% of the water consumption (see Supp Fig 2). Therefore, developing solutions that reduce the size of these systems can significantly reduce resource consumption and improve the process performance metrics. To identify the key variables in the back-end section, we performed sensitivity analysis by varying four of our process variables by ±25% and observing the changes in several process metrics (Figure ). Batch time was the least significant variable to optimize (Figure ), likely due to the assumed 3-day batch time causing a 25% swing to be miniscule. We investigated cyanobacteria biomass yield, as we assumed that optimizing photon flux to biomass would improve efficiency; however, the economic metrics do not support this (Figure ). The design of the b-PBR seemed like a promising direction, with the surface area to volume ratio (SA:V) improving resource use by 7-40% (Figure ). However, economic metrics were less impacted by SA:V, with a less than 10% change in PRC and TAC. The most promising variable identified was P density, which describes the amount of P absorbed per kg of biomass (Figure ). Specifically, we found that increasing P density by 25% improves economic metrics by 10-11% and resource usage by 20-34%. Across all six process metrics considered, P density had the largest impact of all the modeled variables, motivating us to design a cyanobacteria strain with optimized P density as the most likely path to improving overall process performance.