Improving the Convergence of the SELEST Identifiability Procedure

Abstract

SELEST is a procedure for identifiability of parameters in which selection and estimation steps are simultaneous, ensuring a well-conditioned estimation problem for a subset of identifiable parameters. Nevertheless, since SELEST is based on local sensitivity analysis, the identifiability criteria are dependent on the parameters initial values, requiring intensive parameters evaluation. In order to improve the convergence of the algorithm, we propose to update the values of the selected parameters and their sensitivity submatrix when re-ranking the remaining parameters. Therefore, the parameters estimations are performed using more appropriate values than the initial estimates. Two cases studies illustrate the performance of the proposed procedure: a hypothetical model, and an enzymatic hydrolysis model. Results demonstrate that the proposed modifications improved the performance of the algorithm, reducing the computational time significantly.