Development of a high-fidelity digital twin using the discrete element method for a continuous direct compression...

Area of Science:

• Pharmaceutical Engineering
• Computational Fluid Dynamics
• Materials Science

Background:

• Direct compression is a key process in pharmaceutical manufacturing.
• Developing robust control strategies requires accurate process simulation.
• Digital twins offer a powerful platform for virtual testing and optimization.

Purpose of the Study:

• To develop a high-fidelity digital twin for direct compression drug product manufacturing.
• To validate the digital twin's predictive capabilities using experimental data.
• To establish a calibration method for Discrete Element Method (DEM) models in pharmaceutical processes.

Main Methods:

• Development of a digital twin platform simulating pharmaceutical equipment and direct compression processes.
• Detailed material characterization using bulk density, compression, shear cell, and rotating drum tests.
• Application and calibration of a cohesive DEM contact model using experimental data.
• Comparison of simulated system response with experimental results for API concentration changes.

Main Results:

• The calibration method successfully differentiated between material batches with varying flowability.
• Small-scale material characterization tests accurately predicted residence time distribution in continuous manufacturing.
• The digital twin demonstrated sensitivity to DEM contact parameters, impacting process prediction.

Conclusions:

• A validated high-fidelity digital twin can effectively support the design and testing of control strategies for direct compression manufacturing.
• The proposed calibration approach enables accurate prediction of process behavior using fundamental material properties.
• This methodology facilitates enhanced understanding and optimization of continuous pharmaceutical manufacturing processes.