A Critical Review of Algae Process Optimization Using Design of Experiment Methodologies

Climate change, limited fossil resources and steady population growth drive industrial development of sustainable biomass based processes for production of fuels, platform and specialty chemicals. In this context phototrophic microalgae cultivation processes are poised to play a central role for development of next generation bioprocesses due to high cellular growth rates and excellent yields of value adding products. Additionally, microalgae cultivation does not compete with food production and does not require agricultural land. However, industrial scale realization of microalgae cultivation has been hampered by technical issues such as optimal light and gas supply and the economic configuration of the bioreactor system. The resulting multi-parameter space complicates a targeted process optimization. To streamline experimental process optimization mathematical modeling using design of experiment (DoE) methodologies can be applied. Initial experimental data can be used to refine primary mathematical models. Therefore, DoE can be applied for consolidation of an iterative process optimization. However, DoE model choice and optimization in a dependent multi-parameter space is complex and requires diligent analysis of model design and parameter outcome. Testing data for normality is essential to derive a viable model and experimental data. If these prerequisites are not met misguided approaches to process optimization may result. In this study we reanalyze published data and demonstrate methods to streamline interpretation of DoE data. Further, methodologies and strategies for data transformation are presented that improve model evolution. To the best of our knowledge, this is the first time that a sequential strategy for DoE model evolution was applied to an algae cultivation process. Guiding iterative process optimization for algae cultivation by robust DoE models will significantly contribute to accelerate process scale-up and time to market scenarios. Ultimately, these factors determine success of an industrial process design.