Kinetic Exponent Network Analysis

Significant efforts are underway in Europe to re-shore the synthesis of active chemical ingredients (API’s) to ensure the safety and availability of medicine for its citizens. This goes hand in hand with innovations and process intensification.

For example, doing multiple chemical reaction steps in one go, and/or by using continuous flow reactors that are more energy and solvent efficient, with a smaller footprint, scalable (by numbering-up), and cheaper to build and maintain.

A key issue, however, of doing multiple reactions at once is that different reactants and products can react, thus forming a chemical reaction network (CRN).

While CRN’s are ubiquitous in biological systems, they are mostly avoided in industry due to their complex behavior and effort needed to understand and quantify them.

In this project, I propose a new method to analyse experimental kinetic data in order to directly determine the structure of the CRN.

To this end, I will develop Kinetic Exponent Network Analysis (KENA) to study CRN’s that are of high interest for academia as well as industry.

The change in concentration at early times can be described with power laws, from which the network connectivity of species population and thus the CRN structure can be deduced.

KENA could then optimize the chemical conversion and selectivity of the desired products in CRN’s, contributing to efficient API re-implementation in Europe.