Meteorological Data-Based Optimal Control Strategy for Microalgae Cultivation in Open Pond Systems

Abstract

Outdoor biofuel production from microalgae is a complex dynamical process submitted to climatic variations. Controlling and optimizing such a nonlinear process strongly influenced by weather conditions is therefore tricky, but it is crucial to make this process economically sustainable. The strategy investigated in this study uses weather forecast coupled to a detailed predictive model of algal productivity for online optimization of the rates of fresh medium injection and culture removal into and from the pond. This optimization strategy was applied at various climatic conditions and significantly increased productivity compared to a standard operation with constant pond depth and dilution rate, by up to a factor of 2.2 in a Mediterranean climate in summer. A thorough analysis of the optimizer strategy revealed that the increase of productivity in summer was achieved by finding a trade-off between algal concentration to optimally distribute light and pond temperature to get closer to optimal growth temperature. This study also revealed that maintaining the temperature as high as possible is the best strategy to maximize productivity in cold climatic conditions.