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Despite growing applications being reported both in academia and industry, continuous flow chemistry remains a relatively untaught field across most chemistry undergraduate courses. This is particularly true in laboratory practical classes, where it is often deemed simpler to carry out synthetic reactions in traditional batch mode using round-bottomed flasks. Herein, we report the development of an undergraduate project that utilises cheap and readily available materials to construct continuous flow reactors. The students compare the performance of different types of reactors and conditions in a biphasic selective acetylation of a symmetrical diamine. Throughout the investigation, the students can vary multiple parameters as they optimise the reaction, thus actively learning and readjusting them based on their improved understanding. The experiments give the students an appreciation of continuous flow techniques in comparison to batch. [ABSTRACT FROM AUTHOR]

Downstream purification of products and intermediates is essential for the development of continuous flow processes. Described herein, is a study on the use of a modular and reconfigurable continuous flow platform for the self-optimisation of reactive extractions and multi-step reaction-extraction processes. The selective extraction of one amine from a mixture of two similar amines was achieved with an optimum separation of 90%, and in this case, the black-box optimisation approach was superior to global polynomial modelling. Furthermore, this methodology was utilised to simultaneously optimise the continuous flow synthesis and work-up of N-benzyl-α-methylbenzylamine with respect to four variables, resulting in a significantly improved purity. [ABSTRACT FROM AUTHOR]

• Multi-objective algorithm applied to the self-optimisation of complex pharmaceutical processes. • Algorithm targeted automated reactor productivity and downstream purification. • Pareto front rapidly generated for three competing objectives. There has been an increasing interest in the use of automated self-optimising continuous flow platforms for the development and manufacture in synthesis in recent years. Such processes include multiple reactive and work-up steps, which need to be efficiently optimised. Here, we report the combination of multi-objective optimisation based on machine learning methods (TSEMO algorithm) with self-optimising platforms for the optimisation of multi-step continuous reaction processes. This is demonstrated for a pharmaceutically relevant Sonogashira reaction. We demonstrate how optimum reaction conditions are re-evaluated with the changing downstream work-up specifications in the active learning process. Furthermore, a Claisen-Schmidt condensation reaction with subsequent liquid-liquid separation was optimised with respect to three-objectives. This approach provides the ability to simultaneously optimise multi-step processes with respect to multiple objectives, and thus has the potential to make substantial savings in time and resources. [ABSTRACT FROM AUTHOR]