Data based analysis and operational strategy development for two-stage microalgal cultivation with salt stress-induced lipid accumulation

Abstract

Current efforts on the optimization of the two-stage cultivation of microalgae using stress-induced lipid accumulation have mostly focused only on the lipid induction stage. Although recent studies have shown that stress-induced lipid accumulation is affected by the physiological status of the cells harvested at the preceding cultivation stage, but this issue has hardly been examined hitherto. In spite of the advantages of stress-induced lipid accumulation, it is an imperfect technique that has a stress control problem that might become a greater risk when the significant variabilities in the final quality of biomass exit in outdoor cultivation. However, if the amount of lipid induction can be effectively controlled despite the variations in the incubation condition and raw material consumed, the lipid induction can be worthy of consideration. Thus, such a study needs to be carried out in a systematic way in order to induce lipid accumulation in a consistent and predictable manner regard for variances seen at the cultivation stage. In this dissertation, a predictive model to estimate lipid accumulation using salt stress is developed. The model is designed to use both the physiological status of microalgae and salt stress as variables simultaneously so that the predictive model could determine the appropriate salinity stress according to the physiological status. Thus, the objectives of this dissertation are 1) identifying appropriate measurable proxy variables for the cell's physiological status strongly related to the lipid induction result including growth phase and 2) developing a predictive model for the estimation of the lipid induction in consideration of both the selected proxy variables and the stress condition of the lipid induction stage. 3) Finally, based on the developed model, developing an operational strategy for optimal lipid induction of microalgae in two-step cultivation. First, identification significant proxy variables for the physiological status affecting salt stress-induced lipid accumulation was performed. In addition, a practical guideline to harvest for optimal lipid accumulation was suggested when the measurement is limited. Finally, a predictive model to estimate lipid accumulation was developed using machine learning technique. In conclusion, the results show that the appropriate balance of energy storage mechanism using photosynthesis is necessary to achieve optimal lipid induction, and this balance can be indirectly monitored using the proxy variables for the photosynthetic ability of microalgae.