Autonomous Multi-Step and Multi-Objective Optimization Facilitated by Real-Time Process Analytics.

Area of Science:

• Organic Chemistry
• Chemical Engineering
• Analytical Chemistry

Background:

• Autonomous flow reactors are increasingly used for organic synthesis.
• Current limitations exist in analyzing complex chemical reactions and optimizing processes.
• Need for efficient and timely analysis of reaction outcomes in flow chemistry.

Purpose of the Study:

• To develop a modular platform for efficient and timely analysis of reaction outcomes in autonomous flow reactors.
• To assess self-optimization methodologies for chemical reaction parameter determination.
• To apply the platform to optimize the synthesis of a pharmaceutically relevant compound.

Main Methods:

• Development of a modular platform integrating rapid flow Nuclear Magnetic Resonance (NMR) and Fourier-Transform Infrared (FTIR) measurements.
• Application of chemometric modeling for reaction outcome analysis.
• Testing with a four-variable single-step nucleophilic aromatic substitution reaction for methodology assessment.
• Extension to a seven-variable two-step optimization for synthesizing edaravone.

Main Results:

• The self-optimization approach with minimal prior knowledge efficiently identified optimal reaction parameters in a short operational time.
• The platform successfully optimized a complex seven-variable, two-step synthesis.
• Achieved >95% solution yield for the intermediate in edaravone synthesis.
• Attained a space-time yield of up to 5.42 kg L⁻¹ h⁻¹ for the pharmaceutically relevant product.

Conclusions:

• The developed modular platform enables efficient and autonomous optimization of complex organic reactions in flow.
• Self-optimization strategies are effective for minimizing background knowledge and operational time.
• The platform demonstrates significant potential for accelerating the synthesis of active pharmaceutical ingredients.