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Inline Analytics and Chemometric Tools for Kinetic Modeling in Flow Chemistry
von Lisa SchulzFlow chemistry is an expanding sector within the chemical industry, offering benefits such as process intensification, enhanced safety, and higher productivity compared to traditional batch processes. The combination of continuous processes with inline analytics and chemometric tools facilitates more efficient acquisition and evaluation of kinetic data for process development, especially since errors caused by sampling are avoided and unstable intermediates can be monitored.
In this work, principal component analysis (PCA) and multivariate curve resolution (MCR) are demonstrated as chemometric tools for efficient solvent selection and calibration-free kinetic investigations for model-based scale-up prediction.
A PCA-assisted solvent selection is presented to identify solvents with best performance for flow conditions with minimal experimental effort.
For calibration-free kinetic modeling, MCR is adapted for the investigation of an imine synthesis in an oscillating segmented flow reactor. Two MCR methods are evaluated: soft modeling with soft constraints, and hard modeling with a physico-chemical model as a hard constraint.
While both approaches yield similar results, hard modeling effectively addresses rotational ambiguity, which means that there is no unique solution but a range of feasible solutions. Additionally, hard modeling enables modeling of non-detectable components. The hard modeling approach is further developed to analyze multiset data from various temperatures and initial concentrations.
This advanced MCR method is applied to a Sonogashira reaction, providing detailed kinetic parameters for model-based scale-up prediction. A new strategy for calibration-free concentration monitoring during scale-up is presented.
Based on these results, a general procedure for kinetic modeling and model-based scale-up prediction is developed and applied for the ring-opening of gamma-valerolactone.
In this work, principal component analysis (PCA) and multivariate curve resolution (MCR) are demonstrated as chemometric tools for efficient solvent selection and calibration-free kinetic investigations for model-based scale-up prediction.
A PCA-assisted solvent selection is presented to identify solvents with best performance for flow conditions with minimal experimental effort.
For calibration-free kinetic modeling, MCR is adapted for the investigation of an imine synthesis in an oscillating segmented flow reactor. Two MCR methods are evaluated: soft modeling with soft constraints, and hard modeling with a physico-chemical model as a hard constraint.
While both approaches yield similar results, hard modeling effectively addresses rotational ambiguity, which means that there is no unique solution but a range of feasible solutions. Additionally, hard modeling enables modeling of non-detectable components. The hard modeling approach is further developed to analyze multiset data from various temperatures and initial concentrations.
This advanced MCR method is applied to a Sonogashira reaction, providing detailed kinetic parameters for model-based scale-up prediction. A new strategy for calibration-free concentration monitoring during scale-up is presented.
Based on these results, a general procedure for kinetic modeling and model-based scale-up prediction is developed and applied for the ring-opening of gamma-valerolactone.