Kinetic parameter estimation based on spectroscopic data with unknown absorbing species

Abstract

In many studies, kinetic parameter estimation from spectroscopic data is performed with the absorbing species known beforehand, as this provides a straightforward link between the reaction models to the spectroscopic data. In practice, however, the absorbing species are generally unknown and they are only estimated based on professional experience and prior knowledge of the kinetic reaction. In this work, we propose an optimization strategy with both continuous and discrete decision variables in order to estimate kinetic parameters from spectroscopic data with unknown absorbing species. Also included in our approach is an estimability analysis for kinetic parameters based on the Gram‐Schmidt orthogonalization procedure, along with covariance estimation. Four case studies were considered, which demonstrate the effectiveness of our approach. The first and second have simulated data and illustrate our approach with known solutions. The third and fourth are based on actual experiments from spectroscopy data sets. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3595–3613, 2018

Reduced Hessian based parameter selection and estimation with simultaneous collocation approach

“…The nominal values for all the parameters are 20% positively biased from the true values, that is, (each value is multiplied by 1.20). For the proposed approach, when Step 4 is implemented, we get S e = {K [12], K [28], K [10], K[36], K[37], K[42],…”

“…If the tolerances for η is changed to 0.5, the estimable parameters reported by the proposed approach in S e are K [28], K [12], K [10],…”

“…On the other hand, the orthogonalization approach 5 is simple to implement and interpret. It is the most widely used technique for parameter selection [7][8][9][10][11][12] and its effectiveness is identified by Chu and Hahn 4 and can be regarded as a sequential method that maximizes the D criterion at each step. The drawback is that it is not easy to set a proper projected vector norm threshold or cut-off value for separating the estimable parameters from the others.…”

Kinetic Parameter Estimation from Spectroscopic Data for a Multi-Stage Solid–Liquid Pharmaceutical Process

“…Apart from using soft- or hard-modeling techniques, hybrid techniques can be used, e.g., where MCR-alternating least squares (MCR-ALS) is combined with a kinetic model description . This methodology is widely used in academia, but due to its challenges related to large datasets it is not widely used for industrial applications …”

“…3 This methodology is widely used in academia, but due to its challenges related to large datasets it is not widely used for industrial applications. 4 Moreover, Alsmeyer et al 5 introduced an indirect spectral hard-modeling approach which uses parameters for the descriptive models describing the individual spectra. This approach requires that all the individual absorbance profiles are known in advance, and thus it cannot describe the behavior of intermediates or non-absorbing species.…”