This work presents and evaluates an approach to obtain and model kinetic data by combining a microreactor setup and real-time reaction monitoring through inline Fourier transform infrared spectroscopy with nonsteady-state conditions and self-modeling curve resolution (SMCR). Two model reactions, imine synthesis of benzaldehyde with benzylamine and deprotonation reaction with n-butyllithium, serve as a proof of concept and additionally demonstrate the method’s broad range of application, which includes simple reactions as well as complex mechanisms. Subsequent replications of the model reactions above (in terms of collection and modeling of kinetic data) using a more common approach (steady-state conditions and spectra evaluation using calibration curves) outline that the presented approach possesses greater time-efficiency compared to traditional methods (based on batch or steady-state studies), but that reliability of the resulting kinetic parameters should be reviewed carefully. However, when quick estimates are needed (analyzing the elementary reaction mechanism rather than developing a detailed scale-up model), research and industry alike may achieve significant time and cost savings through applying the outlined approach. To guide them in using this method in the most effective manner, this paper concludes by comparing two types of SMCR, soft- and hard-modeling, and argues for combining them.

Scale-up predictions from a continuous flow micro-(lab) to milliscale (pilot or production) are important but not trivial. To overcome the necessity of time-consuming trials on the pilot or production scale, this work presents a modelbased approach that builds on prior lab experiments. However, it also improves the understanding of the involved chemical process. A complete process development for a highly exothermic organometallic reaction is conducted. Kinetic studies on a lab scale, using inline FT-IR measurements, constitute the basis for a systematic scale-up approach. Subsequently, the scale-up from microreactors (inner diameter of 0.5 mm) to milliscale pilot reactors (inner diameter of 2 mm) through increasing the channel diameter and flow rates is investigated. Model-based scale-up predictions are presented, including heat and mass balances.

Self-optimisation constitutes a very helpful tool for chemical process development, both in lab and in industrial applications. However, research on the application of model-free autonomous optimisation strategies (based on experimental investigation) for complex reactions of high industrial significance, which involve considerable intermediate and by-product formation, is still in an early stage. This article describes the development of an enhanced autonomous microfluidic reactor platform for organolithium and epoxide reactions that incorporates a successive combination of inline FT-IR spectrometer and online mass spectrometer. Experimental data is collected in real-time and used as feedback for the optimisation algorithms (modified Simplex algorithm and Design of Experiments) without time delay. An efficient approach to handle intricate optimisation problems is presented, where the inline FT-IR measurements are used to monitor the reaction’s main components, whereas the mass spectrometer’s high sensitivity permits insights into the formation of by-products. To demonstrate the platform’s flexibility, optimal reaction conditions of two organic syntheses are identified. Both pose several challenges, as complex reaction mechanisms are involved, leading to a large number of variable parameters, and a considerable amount of by-products is generated under non-ideal process conditions. Through multidimensional real-time optimisation, the platform supersedes labor- and cost-intensive work-up procedures, while diminishing waste generation, too. Thus, it renders production processes more efficient and contributes to their overall sustainability. Graphical abstract

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