Abstract

Traditional optimization studies are mainly based on the know-how of chemists and eventually use the principles of Design of Experiment (DoE) methods. While DoE can be automated to speed-up the screening of predetermined experimental conditions, it requires a high number of experiments to locate an optimum. DoE has been also studied with flow reactors for the efficient optimization of chemical reactions. A much more appealing strategy consists in integrating a feedback with optimization algorithms. This approach enables optimization systems to analyze previous reactions data in order to adapt and modify the following experiments in a short time frame. The challenge associated with intelligent optimization systems results in the need to perform a constrained optimization without a priori on the value of reaction parameters, making the optimization of complex reactions a challenge beyond classical optimization techniques.

We will discuss reaction optimizations in flow, using of a modified Simplex algorithm. The beneficial properties of flow reactors associated to the power of optimization algorithms for the fine-tuning of experimental parameters, allowed reactions to proceed in conditions unable to promote the coupling through traditional batch chemistry. Our modified simplex algorithm showed great flexibility since the experimental conditions could be tuned according to the nature of three different objective functions which could be either the yield, the throughput or the production cost.