107: Information-rich flow chemistry: A multi-modal, multi-sensor pat framework for real-time green process development and optimization

Abstract: The design of sustainable chemical processes increasingly demands a shift from empirical, trial-and-error experimentation toward information-rich, data-driven development. Continuous-flow chemistry provides an enabling platform for greener and intensified manufacturing due to enhanced heat and mass transfer, improved inherent safety, reduced solvent inventories, and precise control of residence time and mixing. However, in laboratory and early-stage development, the benefits of flow processing are often constrained by limited analytical observability when reaction understanding depends primarily on intermittent sampling and offline chromatographic analysis. Such workflows introduce latency between process perturbation and measurement, obscure transient behaviour and short-lived intermediates, increase solvent and consumables usage, and reduce the mechanistic information extracted per experiment. Here, we propose a multi-spectra, multi-sensor Process Analytical Technology (PAT) framework that transforms flow reactors into self-observing, information-rich reaction systems. The platform integrates complementary spectroscopic modalities—UV/Vis, near-infrared (NIR), mid-infrared (mid-IR), and/or Raman spectroscopy—with synchronized process measurements (e.g., temperature, pressure, flow rate, and pH) to generate high-dimensional, time-resolved datasets describing the coupled evolution of chemical composition and physical state. Multivariate chemometric models and AI-assisted data fusion convert these data streams into quantitative concentration trajectories, kinetic and transport-relevant parameters, and uncertainty-aware soft sensors suitable for monitoring and control. The resulting workflow accelerates process understanding and optimization while advancing green chemistry objectives by reducing experimental iterations, minimizing solvent-intensive sampling, enabling early detection of impurities and deviations, and supporting energy- and material-efficient operation through real-time decision-making.