Design, Execution, and Analysis of Time-Varying Experiments for Model Discrimination and Parameter Estimation in Microreactors

Abstract

Time-varying, or dynamic, experiments can produce richer data sets than sequences of steady-state experiments using less material and time. A case study demonstrating this concept for microreactor experiments is presented. Beginning with five kinetic model candidates for the reaction of phenylisocyanate with t-butanol, an initial dynamic experiment showed that two of the five models gave a similar quality of fit to the experimental data, whereas the remaining three gave significantly poorer fits. Next an optimal experiment was designed to discriminate between the remaining two models. This drove the two models to differ significantly in quality, leaving a single model and a set of kinetic parameter values that adequately described the data. This method can be applied to future kinetic studies to reduce material use and experimental time while validating a dynamic model of the physics and chemical kinetics.