A computational methodology based on inverse modeling and metaheuristics is presented for determining the best parameters of kinetic models aimed to predict the behavior of biomass depolymerization processes during size scaling up. The Univariate Marginal Distribution algorithm, particle swarm optimization, and Interior-Point algorithm were applied to obtain the values of the kinetic parameters (KM and Vmax) of four mathematical models based on the Michaelis−Menten equation: (i) Traditional Michaelis−Menten, (ii) non-competitive inhibition, (iii) competitive inhibition, and (iv) substrate inhibition. The kinetic data were obtained from our own experimentation in micro-scale. The parameters obtained from an optimized micro-scale experiment were compared with a bench scale experiment (0.5 L). Regarding the metaheuristic optimizers, it is concluded that the Interior-Point algorithm is effective in solving inverse modeling problems and has the best prediction power. According to the results, the Traditional model adequately describes the micro-scale experiments. It was found that the Traditional model with optimized parameters was able to predict the behavior of the depolymerization process during size scaling up. The methodology followed in this study can be adopted as a starting point for the solution of future inverse modeling problems.