This study investigates the use of global sensitivity analysis combined with stochastic optimization for improving the conversion of biomass in an inverted cyclone reactor. A validated mathematical model of the process permits to develop a model-centric framework that analyzes the most important process variables and design parameters, for later selecting the combination that achieves the highest biomass conversion rate. The implemented global sensitivity analysis, which is a variation of the Sobol method, permits to identify the most influential variables in biomass conversion. Thereafter, the important process variables of the system are optimized respecting inequality and equality constraints towards the maximization of the conversion, providing an improved reactor setup. The proposed framework has the potential of evaluating different biomass conversion models that aim to transform biomass into value-added chemicals through different objective functions.