No abstract

Continuous flow reactors (CFRs) are an emerging technology that offer several advantages over traditional batch synthesis methods, including more efficient mixing schemes, rapid heat transfer, and increased user safety. Of particular interest to the specialty chemical and pharmaceutical manufacturing industries is the significantly improved reliability and product reproducibility over time. CFR reproducibility can be attributed to the reactors achieving and maintaining a steady state once all physical and chemical conditions have stabilized. This work describes the implementation of a smart CFR with univariate physical and multivariate chemical monitoring that allows for rapid determination of steady state, requiring less than one minute. Additionally, the use of process analytical technology further enabled a significant reduction in the time and cost associated with offline validation methods. The technology implemented for this study is chemistry and hardware agnostic, making this approach a viable means of optimizing the conditions of any CFR.

The Moffatt-Swern oxidation (MSO) is a multistep, versatile, metal-free reaction by which alcohols are transformed into aldehydes and ketones. Batch MSO requires low temperatures (−70°C) due to a highly exothermic reaction step that generates intermediates. This work shows that a rigorous investigation of the MSO in batch can be used as a stepping-stone to its implementation in a continuous-flow reactor (CFR). This work has two parts: the first part details the investigation of MSO in batch; the second covers the translation of the knowledge derived from batch to a CFR. The MSO batch reaction was performed under cryogenic conditions with real-time process monitoring. The reaction was monitored with Raman spectroscopy and could be tracked throughout the reaction. All concentrations were validated using offline high-performance liquid chromatography (HPLC) and gas chromatography-mass spectrometry (GC-MS). Two configurations of the CFR were produced. Configuration 1 used the traditional batch methodology in terms of reagent addition and reaction conditions. Configuration 2 used the information derived from the batch reaction, changing the order of the reagent addition and increasing the temperature of the reactor. Real-time quantitative monitoring of chemical yield in the CFR was demonstrated via Raman spectroscopy and partial least squares (PLS) regression modeling. Reaction yield was accurately predicted every 15 s, reducing the need for chromatographic validation once the model was built. Configuration 2 was shown to perform comparably to configuration 1 at low temperature and far outperforming it at higher temperatures. Both CFR configurations performed significantly better than the batch setup in terms of temperature and yield, as was expected.