Algae have been a hot topic in recent years due to their potential for several reasons. They have been proposed as an alternative for fossil fuels, a superfood for consumers, a potential CO2 absorber to reduce green house gas emission, feedstock for nutritional and chemical industry, a waste water treatment technique and more. However, the large scale implementation of algae in industry has not yet been realized. There is a bridge to be built between the biological work and the scale up to industrial size. In the scientific community there are a lot different approaches and visions for predicting algae growth. No common accepted engineering approach exists. The models found in literature vary from models which only account for illumination to models which account for more then 20 parameters. An effective model is required to close the gap between fundamental research and the scale up to industrial size. A modelling approach is chosen which divides the photobioreactor into zones of constant light intensity and predicts algae growth by the light intensity in that specific zone. First a basic model with only light as major dependent variable is built to get an understanding about the dynamics in a photobioreactor. This model is extended for nutrient limitations. A submodel is added to simulate the uptake and loss of CO2 by the algae and the medium. The model is made with Mathworks Matlab. The model has been validated using data from literature for 3 different photobioreactors with different types of algae. These photobioreactors are: a LED illuminated 0.8 m3 raceway pond using Chlorella sp., a sun illuminated 0.53 m3 circular pond using Nannochloropsis Salina and a sun illuminated 11.9 m3 raceway pond using Nannochloropsis Salina. A 1 litre photobioreactor has been constructed to validate the model with experiments, for this the algae specific parameters of the used algae are required. To determine these parameters a test arrangement is constructed. Experiments with the test arrangement and the photobioreactor have not yet been performed. Performing qualitative and founded tests require time and effort. This is for future work.