Rapid multistep kinetic model generation from transient flow data.

Area of Science:

• Chemical Engineering
• Reaction Kinetics
• Process Chemistry

Background:

• Kinetic model generation is traditionally resource-intensive and requires specialized expertise.
• Optimizing chemical processes often necessitates accurate kinetic data, which can be challenging to obtain efficiently.

Purpose of the Study:

• To develop and demonstrate an efficient method for generating reaction profiles and kinetic models using continuous-flow chemistry.
• To reduce the time and resources required for kinetic model development.
• To enable early process optimization and reduce scale-up risks.

Main Methods:

• Utilized a state-of-the-art continuous-flow platform with an automated linear gradient flow ramp.
• Collected experimental data for multistep aromatic nucleophilic substitution reactions using online High-Performance Liquid Chromatography (HPLC).
• Operated under varied conditions (3 concentrations, 4 temperatures) to generate multiple reaction profiles.

Main Results:

• Generated 16 reaction profiles in under 3 hours.
• Accurately fitted kinetic parameters (rate constants, activation energies) with less than 4% uncertainty.
• Quantified and demonstrated that dispersion-induced errors in rate constants were 5% or lower.

Conclusions:

• The continuous-flow method significantly accelerates kinetic model generation.
• The approach allows for early identification of process sensitivities and facilitates in silico optimization.
• This method reduces scale-up risks by providing rapid, accurate kinetic insights.