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Microalgae processing represents one of the most promising new technologies for sustainable production of a wide range of commodities and value-added products, including cosmetics, pharmaceuticals and nutraceuticals. Moreover, at a larger time horizon, microalgae are expected to contribute for fossil carbon replacement with renewable carbon, especially for supplying green chemicals and liquid biofuel in the transport sector. Nevertheless, much research is still needed in order to make this potential new energy source a practically and economically feasible technology, since all the existing technological assessments are based on specific assumptions or gross estimates of productivity, derived by extrapolation of laboratory-scale data. The development of reliable mathematical models predicting both the behavior of large-scale outdoor microalgae culture and the underlying multiple time-scale biophysical and chemical processes is therefore necessary. These models are valuable tools to support both system design and operation optimization, with consequent potential increase of the process profitability. This Thesis aims at investigating the complex behavior of microalgae growth by following two main approaches. The first objective was to extend an existing growth model of marine water alga Nannochloropsis Salina describing photosynthetic efficiency through chlorophyll fluorescence dynamics. This microscale model integrates photoproduction, photoregulation and photoinhibition processes in a semi-mechanistic way, but it is limited to the description of the most significant photosystem PSII. The proposed model extension aims at describing the complete electron transport, together with the dynamics of each protein complex involved in the photosynthetic process, through absorbance data-based calibration/validation. The results show that the calibrated model is capable of accurate quantitative predictions of the photosynthetic transport chain paths under a wide range of transient light conditions. The second contribution objective was to develop a macro-scale model for Chlorella Vulgaris cultivation in open pond systems by coupling existing growth/temperature sub-models with real meteorological data. The utilization of this dynamic model will underline the benefits of model building activities on practical process optimization, since a reduced set of `rules of thumb' was extracted by different simulations done at different weather conditions. The proposed optimization strategy significantly increased productivity compared to standard operation at constant dilution rate and pond depth, by up to a factor 2.2. Furthermore, a deeper insight into optimal operation in case of inaccurate forecasts has been developed and discussed. The different strategies proposed can guarantee both high productivity and feasible operation in case of inaccurate weather forecasts. The resulting control strategies, despite the high amount of water required, can prevent culture death conditions due to unpredicted high temperatures.