(430b) Optimization-Based Strategies for Spectral Analysis and Kinetic Modeling

Sampling of the reaction system using spectroscopic monitoring can be done using either in situ or ex situ methods. In situ methods are safer and can provide more accurate information, however, the information collected can be challenging to analyze for multicomponent mixtures (Pell et al.). The spectra collected from these methods (such as infrared, ultra-violet, Ramen, etc.) can cause issues since little may be known about the proposed reaction or there may be overlapping species or unwanted solvent contributions.

A multitude of data analysis approaches exist in academia and industry. These can be broadly categorized into approaches which postulate a kinetic model for the reaction system, attempt to solve the system without a kinetic model, or a hybrid combination of the two. Each method ultimately has its own pros and cons, and often analysis of these systems requires use of expert opinion. Recently published examples of these problems and their difficulties can be found in works by Pell et al. and Chen X. et al.

A proposed alternative method to these modeling approaches is to simultaneously obtain the reaction kinetic parameters with the curve resolution, based on an optimization modeling platform called KIPET (KInetic Parameter Estimation Tool). The KIPET approach is derived from maximum likelihood principles and uses nonlinear programming techniques and collocation methods to simultaneously solve a proposed reaction system (Schenk et al, W. Chen, L.T. Biegler, and S.G. Muñoz), which has been shown to outperform traditional approaches like MCR-ALS.

We focus on applying these techniques to the previously published work of Chen X. et al. We examine a polymerization reaction to exhibit these methods and investigate deviations from Beer’s Law that occur due to a high monomer concentration.