(342a) Kinetic Parameter Estimation from Multiple Spectroscopic Datasets with or without Unwanted Spectral Contributions Using Kipet
AIChE Annual Meeting
Tuesday, November 12, 2019 - 12:30pm to 12:55pm
In a breakthrough approach, Chen et al. (2016) presented a unified framework based on maximum likelihood principles, nonlinear optimization techniques, and orthogonal collocation methods to solve the systems of differential equations. The approach first estimates the variances related to both system variable noise and measurement noise through an iterative optimization-based algorithm and then simultaneously determines the concentration profiles, kinetic parameters, and individual speciesâ absorbance profiles using the nonlinear programming solver, IPOPT. Using the sensitivity properties at the optimal solution, the technique is also able to obtain the covariance matrix and confidence intervals of the estimated parameters. This approach was recently developed into an open-source Python-based package called KIPET (Kinetic Parameter Estimation Toolkit) (Short et al., 2019). KIPET makes use of Pyomo (Hart et al., 2017) as the algebraic modeling language, however users need not be experts in Python nor Pyomo to make use of the many parameter estimation tools therein.
In this work we develop a novel extension to the framework presented by Chen, et al. (2016, 2019) where multiple experimental datasets are simultaneously used to determine the reaction kinetic parameters, concentration profiles in each experimental run, as well as individual species absorbances. In certain experimental setups, new reactants and reactions are introduced and in other examples, different process conditions with different feed times and temperatures are used. In this way, local parameters are introduced in certain datasets, with global parameters linking each experimental dataset. In addition to simultaneously estimating both local parameters and global parameters, we also introduce the ability to determine unwanted contributions that are either time-dependent or time independent. The new approach is demonstrated through a set of case studies that show the utility of performing parameter estimation across datasets as well as the effects of explicitly including unwanted contributions into the model.