Automated Residence Time Distribution Characterization in Flow Reactors: A Unified Framework toward Digital Twins

Characterizing flow reactors is crucial for predicting the steady-state conditions under which they operate, thereby enabling efficient transfer of chemical processes. The residence time distribution (RTD) is a vital parameter for understanding and optimizing flow reactor performance. This work introduces a robust methodology for RTD characterization that is applicable to a wide range of reactors and fluids, accommodating variations in viscosity and density. By employing a skew-normal distribution to model reactor responses, the methodology demonstrates high robustness and versatility. The workflow integrates automated data analysis through a Python script, facilitating extraction of key descriptors that define the reactor’s RTD under specific operating conditions. Importantly, the experimental setup relies on a single spectrometer to simultaneously measure inlet and outlet signals, ensuring precise time synchronization and enabling deconvolution when necessary. The use of the Rose Bengal/TBACl tracer system extends the applicability to organic solvents representative of reaction mixtures. This comprehensive approach was successfully validated on three reactor types: a coiled tubular reactor, a dynamically stirred tubular reactor, and a Corning AFR LF reactor. The resulting dispersion and skewness parameters can be correlated with fluid physical properties, enabling prediction of the RTD for characterized reactor systems under varying operating conditions. These results represent a key step toward the development of comprehensive digital twins of flow reactors, enabling predictive process optimization and accelerated scale-up in pharmaceutical and fine chemical manufacturing.