The study of the thermal conversion of microalgal biomass has gained significant interest in recent years, as it represents a renewable resource that can be cultivated in areas not allocated for food for human consumption. However, nowadays, reported kinetic studies have primarily focused on determining activation energy, with fewer studies addressing the pre-exponential factor and model order. No comprehensive kinetic schemes have yet been reported that are suitable for theoretical studies, such as scaling rules. This study presents the development of a multistage kinetic analysis for the thermal conversion of Chlorella vulgaris microalgae, derived from non-isothermal analysis data. The effective kinetic triplet for the devolatilization of various pseudo-components, as identified in Differential Thermogravimetry (DTG) analysis —including the pre-exponential factor 𝐴 and activation energy 𝐸— was determined through thermogravimetric experiments performed at heating rates of 5, 10, and 20°C/min, with subsequent statistical validation. A model-free iso-conversional method was employed to determine the effective activation energy E, which ranged from 64.9 kJ/mol for moisture removal to 400 kJ/mol during the primary devolatilization stage and char decomposition. The pre-exponential factor 𝐴 was determined using a model-free compensation and Isokinetic Relationship (IKR) effect procedure. A total of five characteristic reactions zones were identified from the thermograms, indicating that the thermal conversion of Chlorella vulgaris is highly complex and significantly influenced by the thermal behavior of biological pseudo-components such as proteins, carbohydrates, and lipids. To develop a chemical kinetic scheme based on effective kinetic parameters, a simultaneous multistage optimization procedure was performed using the mampel first order reaction model useful for eulerian-eulerian CFD simulations. This approach, based on model-free methods and optimization, has demonstrated a very well predictive capability for the original thermal data, thus offering valuable insights for developing reaction mechanisms, advancing technology applications, and studying scaling laws, especially in processes with low heating rates.