  Simulated Moving Bed (SMB) chromatography is a well-known technique for the
resolution of several high-value-added compounds. Parameters identification and
model topology definition are arduous when one is dealing with complex systems
such as a Simulated Moving Bed unit. Moreover, the large number of experiments
necessary might be an expansive-long process. Hence, this work proposes a novel
methodology for parameter estimation, screening the most suitable topology of
the models sink-source (defined by the adsorption isotherm equation) and
defining the minimum number of experiments necessary to identify the model.
Therefore, a nested loop optimization problem is proposed with three levels
considering the three main goals of the work: parameters estimation; topology
screening by isotherm definition; minimum number of experiments necessary to
yield a precise model. The proposed methodology emulated a real scenario by
introducing noise in the data and using a Software-in-the-Loop (SIL) approach.
Data reconciliation and uncertainty evaluation add robustness to the parameter
estimation adding precision and reliability to the model. The methodology is
validated considering experimental data from literature apart from the samples
applied for parameter estimation, following a cross-validation. The results
corroborate that it is possible to carry out trustworthy parameter estimation
directly from an SMB unit with minimal system knowledge.
