Constructing regions of attainable sizes and achieving target size distribution in a batch cooling...

Area of Science:

• Chemical Engineering
• Crystallization Science
• Process Control

Background:

• Ultrasonication offers benefits in crystallization but introduces temperature control challenges.
• Accurate modeling is crucial for optimizing sonocrystallization processes, particularly for temperature-sensitive materials like l-asparagine monohydrate (LAM).

Purpose of the Study:

• To extend a previous population balance model for batch cooling sonocrystallization of LAM.
• To incorporate energy balance equations and a Generic Model Control algorithm for simulating crystallizer temperature control.
• To enable model-based dynamic optimization for particle engineering in LAM sonocrystallization.

Main Methods:

• Developed an enhanced mathematical model integrating energy balance and Generic Model Control (GMC).
• Simulated the crystallizer's temperature controller to track a cooling profile during sonocrystallization.
• Utilized dynamic optimization to determine attainable particle size regions and optimal operating conditions.

Main Results:

• The improved model demonstrated very good closed-loop prediction accuracy.
• The model successfully identified regions of attainable particle sizes for LAM.
• Optimal operating conditions for achieving target crystal size distributions were determined.

Conclusions:

• The enhanced model provides a robust tool for particle engineering in batch cooling sonocrystallization.
• Model-based optimization effectively guides the process towards desired crystal size distributions.
• Experimental validation confirmed the efficiency of this particle engineering approach.