The ability to use a simulation program with the aim of making mass and energy balances in a plant, starting from just raw data and process knowledge, is an ambitious and attractive goal. We propose to address a general formulation for the estimation problem in the framework of a simultaneous modular approach. As raw data are corrected to satisfy mass and energy balances and some parameters are computed to give a unit operation performance diagnosis, the estimation consists both of data reconciliation and parameter identification. It follows that the numerical problem is a minimization of a least square objective function. Constrained minimization is performed with an infeasible path strategy which solves the optimization problem and process balance convergence at the same level, with a nonlinear programming code. The confidence intervals computation, for parameters and corrected values of measurements, is also discussed. A simple flash loop flowsheet example gives a better understanding of the thee etical aspects. A more complex flowsheet has been chosen as a second illustrative example.