During the past decades, intensive research has been pursued on the development of kinetic models to predict process behavior in ethanol production from lignocellulose. These models comprise a large number of parameters which have to be tuned with appropriate experimental data. Therefore, the parameter estimation problem plays an essential role. This work addresses the parameter estimation problem in models representing dilute acid hydrolysis, detoxification, and cofermentation operations in the biochemical production of ethanol from lignocellulosic biomass. The models are represented by sets of differential-algebraic equations (DAEs). Unlike previous approaches, these models account for the main process variables that affect the entire process, specially the final production of bioethanol. These detailed kinetic models, systematically tuned with experimental data, can be used in future studies within a model-based framework that allows performing realistic simulation and optimization aimed at bioethanol process design. A sensitivity analysis has been performed in order to identify the most sensitive parameters. The parameter estimation problem is solved with a simultaneous optimization approach. in which the system of dynamic equations is converted into a set of algebraic ones through orthogonal collocation on finite elements. Thus, estimating the model parameters entails optimizing a weighted least squares objective function subject to the discretized algebraic constraints, resulting in a large-scale nonlinear programming problem (NLP). A good agreement with available experimental data has been obtained with estimated kinetic parameters in each model.