Reactor design optimization using CFD and machine learning for encapsulation processes

Overview

This innovative approach leverages computational fluid dynamics (CFD) and machine learning (ML) to optimize reactor designs, specifically targeting the encapsulation process. By modeling fluid flows with varying viscosities, the solution addresses common scale-up challenges, such as fouling, by identifying optimal reactor geometries that promote efficient flow fields. This methodology is particularly useful for transitioning from batch to continuous production systems, offering significant potential for industrial applications.

Technical specifications

• CFD Modeling: Capable of simulating flow characteristics for materials with a range of viscosities, providing insights into flow dynamics and potential fouling.
• Machine Learning Integration: Utilizes ML to develop reduced-order models from CFD data, facilitating computational efficiency and enabling optimization processes without extensive physical trials.
• Optimization and Control: Employs these models within design optimization problems to identify cost-effective reactor geometries and uses model predictive control (MPC) for real-time process optimization.

Technology readiness level

The project is currently at TRL 2, indicating that the core principles have been formulated and the technology concept has been identified but not yet validated in an operational environment. Future work involves developing and validating CFD models and reduced-order ML models to optimize reactor designs for continuous manufacturing processes.