Flow Modeling, Reactor Engineering, and Process Intensification: From Insight to Performance.

Area of Science:

• Chemical Engineering
• Process Systems Engineering
• Computational Fluid Dynamics

Background:

• Flow modeling and reactor engineering are foundational to modern chemical engineering.
• Process intensification (PI) drives innovation through continuous processing and enhanced efficiency.
• Integrating these disciplines is crucial for optimizing chemical processes.

Purpose of the Study:

• To present critical process metrics (CPMs) as a unifying framework.
• To explore the role of machine learning (ML) in advancing these fields.
• To guide the convergence of flow modeling, reactor engineering, and PI for product and process excellence.

Main Methods:

• Utilizing computational models for flow field visualization and equipment design.
• Applying flow modeling to optimize reactor geometry and internals.
• Leveraging ML and hybrid physics-ML models to analyze CPMs and their attributes.

Main Results:

• CPMs effectively connect flow modeling, reactor engineering, and PI.
• ML models address variability and scale dependence of CPMs.
• Anchoring models to measurable data and decision frameworks ensures robustness.

Conclusions:

• The integration of flow modeling, reactor engineering, and PI, supported by CPMs and ML, leads to superior product and process outcomes.
• This approach facilitates the translation of mechanistic insights into tangible performance improvements.
• Robust and scalable chemical processes are achievable through this synergistic methodology.