As the energy demand is growing rapidly across the globe due to industrialization and higher living standards, along with the depletion of fossil fuel reserves and the effect of climate change more evident than ever [1], there is an urgent need to transition to sustainable methods of energy production. Among various sustainable energy alternatives, bio-energy is expected to play a significant role in the energy transition. Gasification constitutes a thermochemical way of converting carbonaceous feedstock into useful energy. When implemented with biogenic or waste-derived fuels, gasification can provide a promising sustainable energy solution, while it can also simultaneously deal with the landfill waste problem when utilizing feedstocks like Solid Recovered Fuel (SRF). Despite the fact that gasification of coal is often featured in literature, the use of biomass or SRF in gasification practices is still limited. Specific areas like the handling of tars, detailed modeling of the hydrodynamics in the fluidized bed and the freeboard of the reactor, as well as the implementation of chemical kinetics specific to wastederived feedstocks remain underexplored in literature. Furthermore, many studies completely ignore the freeboard region in their models, where critical reactions still occur. Therefore, the aforementioned research gaps are aimed to be answered to in the current thesis project. The purpose of this Master Thesis was to study bubbling fluidized bed gasification and develop a model of its processes using the Python programming language. The open-source software ”Cantera” was used [2], which contains tools for dealing with reaction kinetics, thermodynamics and transport phenomena. To be more exact, the python model includes the chemical reaction kinetics, the mass transfer, and finally the fluid mechanics in the reactor and calculates the composition of the final exiting gas, as well as other physical parameters of the process. The added value of this thesis project lies in the validation of the developed model against experimental results obtained from a series of tests carried out at the TU Darmstadt gasification facility, when experimental gasification campaigns were conducted using Solid Recovered Fuel (SRF) [3]. In addition, this work contributes to the modernization and further development of the model originally presented in Hamel’s PhD thesis [4]. The modeling approach that was followed for the construction of this model was based on a 1-D cell model, where the fluidized bed and the freeboard were discretized in control volumes. Within these control volumes, the local hydrodynamics and the gasification reactions were applied, through mass balance equations. As initial conditions for the gasification model, the outputs of a pyrolysis model were used, applied to the incoming feedstock. The final results of the numerical model show good agreement with the experimental set of data from the TU Darmstadt gasification campaign. More specifically, the model manages to predict with reasonable accuracy the concentrations of four out of the five major syngas species considered. The most notable shortcoming of the model lies in the prediction of the methane content in the final syngas, as this was completely depleted inside the bed, which is an unrealistic behavior given the operating temperature range of the gasification process. This deviation likely lies in the fast kinetics of the steam methane reforming and methane oxidation reactions, pinpointing the need for more accurate reaction kinetics. However, the content of CO, H2, CO2 and H2O were captured reasonably well. In addition, the model predicted a carbon conversion efficiency of 100%, which is unrealistic compared to typical values of around 70-90 % that are observed in real-life applications. This value overestimation lies in the fact that carbon is treated as a pseudo-gaseous species in the model, as well as in the fact that incomplete carbon conversion mechanisms, such as bottom ash removal or particle entrainment with the syngas, are not being considered. Different tests of sensitivity analysis were also conducted to assess the effect of different key parameters on the final product, including the freeboard temperature, the initial gasification conditions, the gaseous distribution through the nozzles and different kinetic parameters. Overall, the model managed to come close to the experimental results, demonstrating its potential in simulating the use of waste-derived fuels in a bubbling fluidized bed gasifier. Therefore, it provides a strong basis for further exploration and development.