Maximizing Productivity and Reducing Environmental Impacts of Full-Scale Algal Production through Optimization of...

The ability to dynamically control algal raceway ponds to maximize biomass productivity and reduce environmental impacts (e.g., land and water use) with consideration of local constraints (e.g., water availability and climatic conditions) is an important consideration in algal biotechnology. This paper presents a novel optimization strategy that seeks to maximize growth (i.e., optimize land use), minimize respiration losses, and minimize water demand through regular adjustment of pond depth and hydraulic retention time (HRT) in response to seasonal changes. To evaluate the efficiency of this strategy, algal productivity and water demand were simulated in five different climatic regions. In comparison to the standard approach (constant and location-independent depth and HRT), dynamic control of depth and HRT was shown to increase productivity by 0.6-9.9% while decreasing water demand by 10-61% depending upon the location considered (corresponding to a decrease in the water footprint of 19-62%). Interestingly, when the fact that the water demand was limited to twice the local annual rainfall was added as a constraint, higher net productivities were predicted in temperate and tropical climates (15.7 and 16.7 g m(-2) day(-1), respectively) than in Mediterranean and subtropical climates (13.0 and 9.7 g m(-2) day(-1), respectively), while algal cultivation was not economically feasible in arid climates. Using dynamic control for a full-scale operation by adjusting for local climatic conditions and water constraints can notably affect algal productivity. It is clear that future assessments of algal cultivation feasibility should implement locally optimized dynamic process control.