Nannochloropsis sp. is a unicellular microalga distinguished by its rapid growth and high lipid content, with lipids comprising up to 70 of its dry biomass, of which over 35 are polyunsaturated fatty acids (PUFAs). Reported biomass productivities for Nannochloropsis sp. in the literature range from 0,1 to 3,2 g/(L d), reflecting a strong dependency on cultivation conditions. This variability in process performance suggests that technical limitations in maintaining optimal process conditions, rather than the physiological capacity of Nannochloropsis are the primary constraint on achieving high and cost effective biomass productivity. The present study aims to parameterize key process variables for Nannochloropsis sp. using an experimental training dataset from a ProviAPT integrated flat panel photobioreactor (PBR) and a mechanistic growth model at the laboratory scale, with particular emphasis placed on identifying a suitable light model that can reliably predict light distribution within the PBR, even at high biomass concentrations.