(621f) Parameter Estimation of Reaction Kinetics from Spectroscopic Data - Developments and Applications

Schenk, C., Carnegie Mellon University
Biegler, L. T., Carnegie Mellon University
Han, L., Pfizer Inc.
Mustakis, J., Pfizer Inc.
The development of drug manufacturing processes involves dealing with spectroscopic data. When dealing with spectroscopic data, the identification of parameters and variances still remains a challenging task. In many cases kinetic parameter identification from spectroscopic data has to be performed without knowing the absorbing species in advance, such that they have to be estimated as well. However, kinetic parameter estimation is important in order to lower production costs, i.e. save measurements and equipment. Furthermore, scaling up from laboratory to industrial level relies on accurate kinetic parameter values.

That is why, we take a closer look at the development of optimization-based procedures in order to estimate the variances of the noise in the system variables and spectral measurements. Then, with the estimated variances we determine the concentration profiles and kinetic parameters simultaneously using adequate strategies. The work is based on the approach proposed by Chen et al. (2016) using maximum likelihood principles for simultaneous estimation of reaction kinetics and curve resolution from process and spectral data. For this a new software environment was developed which is continuously enhanced. This environment is based on Pyomo with different discretization options for the processes described by ordinary differential/differential algebraic equations, such as collocation using Pyomo.DAE and an efficient implementation of variable-order and variable-coefficient BDF methods, implemented in IDA by sundials. The further investigations regarding this software environment, the identification of absorbing species and challenges arising from pharmaceutical processes are presented within this talk. The outcomes are illustrated by several case studies of pharmaceutical processes. The nonlinear programming (NLP) problems, formulated for these applications, are solved using IPOPT and using sIPOPT for the determination of confidence regions for the kinetic parameter estimates.

We gratefully acknowledge Pfizer Inc.’s funding.


W. Chen, L. T. Biegler, and S. G. Muñoz. An approach for simultaneous estimation of reaction kinetics and curve resolution from process and spectral data. Journal of Chemometrics, 30:506–522, 2016. doi:10.1002/cem.2808. URL http://dx.doi.org/10.1002/cem.2808.