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

Abstract

Flow modeling, reactor engineering, and process intensification (PI) have played a major role in shaping modern chemical engineering practice. Early work in flow modeling focused on the use of computational models to visualize flow fields and improve the design of process equipment. During this period, reactor engineering increasingly relied on flow modeling to optimize reactor geometry and internals across scales. The growing emphasis on PI, through strategies such as transforming batch operations into continuous ones, employing structured reactors or alternative energy sources, and enhancing driving forces, further accelerated the development and integration of these approaches. In this perspective, critical process metrics (CPMs) are presented as a useful framework for connecting flow modeling, reactor engineering, and PI and translating mechanistic insight into performance. Recent advances in machine learning (ML) and hybrid physics–ML models provide tools to address distributions, variability, and scale dependence of CPMs, and to relate them to critical quality and performance attributes (CQAs and CPAs). Anchoring models to measurable fingerprints and embedding them within decision frameworks (that account for uncertainty) can enable the convergence of flow modeling, reactor engineering, and PI toward robust and scalable product and process excellence.