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Production of biogas through anaerobic digestion is one pathway to achieving the European Union (EU) goals of reducing greenhouse gas emissions, increasing the share of renewable energy, and improving energy efficiency. In this thesis, two different models (Anaerobic Digestion Model No. 1 and an artificial neural network) are used to simulate a full-scale co-digester in order to evaluate the feasibility of such models. This thesis also includes models of two systems to study the inclusion of microalgae in biogas plants and wastewater treatment plants. One of the studies is a life-cycle assessment in which replacement of the ley crop with microalgae is evaluated. The other study concerns the inclusion of microalgae in case studies of biological treatment in three wastewater treatment plants. Finally, the co-digestion between microalgae and sewage sludge has been simulated to evaluate the effect on biogas and methane yield. The results showed that Anaerobic Digestion Model No.1 and the artificial neural network are suitable for replicating the dynamics of a full-scale co-digestion plant. For the tested period, the artificial neural network showed a better fit for biogas and methane content than the Anaerobic Digestion Model No. 1. Simulations showed that co-digestion with microalgae tended to reduce biomethane production. However, this depended on the species and biodegradability of the microalgae. The results also showed that inclusion of microalgae could decrease carbon dioxide emissions in both types of plants and decrease the energy demand of the studied wastewater treatment plants. The extent of the decrease in the wastewater treatment plants depended on surface volume. In the biogas plant, the inclusion of microalgae led to a lower net energy ratio for the methane compared to when using ley crop silage. Both studies show that microalgae cultivation is best suited for use in summer in the northern climate.