Empirical kinetic modeling of on line simultaneous infrared and calorimetric measurement using a Pareto optimal approach and multi-objective genetic algorithm

With modern online instrumentation it is possible to follow the course of a chemical reaction using a variety of techniques simultaneously (e.g. calorimetry, spectroscopy and gas consumption/production). The data from different instrumentation have different error structures, different information content and often observe different physical phenomenon. Current techniques for the fitting of chemical models to measured kinetic data struggle when faced with data from different types of instrumentation. Rather then combining the data into a single objective function, the ranking of solutions within the genetic algorithm is done using the multi-objective Pareto optimal set. The final output of the algorithm is an optimised Pareto optimal set showing the trade off between the different objective functions for a range of parameter values. The approach has been demonstrated using simultaneously measured calorimetric and mid-IR spectroscopic data of the two-step epoxidation of 2,5-di-tert-butyl-1,4-benzoquinone. The measurements have been evaluated against three different reaction mechanisms in three case studies. The results show that the multi-objective genetic algorithm is a powerful method for the evaluation of a chemical model against data from different sources and for the determination of the appropriateness of a chemical model.