2026/07/01 by Hongyuan Cheng, Aberham Hailu Feyissa
Chemical Engineering · Nursing · Engineering · #Rheology and Fluid Dynamics Studies #Food composition and properties #Granular flow and fluidized beds
paper · doi:10.1111/jfpe.70707
ABSTRACT This review critically examines the current modeling approaches for food and feed extrusion, with particular emphasis on the process variables and physical properties of extrudates. The review addresses four main themes: (i) the key physical quality attributes of extruded pellets; (ii) the effect of extruder geometry, raw material formulation, and process conditions; (iii) modeling of material flow behavior during extrusion; and (iv) quantitative prediction of product quality from extrusion variables. Existing statistical and data‐driven methods are often developed from the operating conditions for a specific extrusion system. Their results cannot be extended across different operating conditions and systems. Empirical and phenomenological models commonly rely on rheological/viscosity‐based principles to relate process conditions to specific extrudate properties, which can explain the process across systems. In contrast, theoretical models aim to capture the underlying physical and chemical transformations during extrusion by numerically solving the governing equations. However, their predictive accuracy is often constrained by uncertain boundary conditions and incomplete thermophysical and rheological data, especially when the material properties evolve continuously throughout the extrusion process. Recent advances in machine learning (ML) offer new opportunities to improve the extrusion modeling and process control. However, the black‐box nature of the ML approach highlights the need for hybrid modeling frameworks that integrate data‐driven learning with physical and chemical knowledge. Overall, future research should prioritize phenomenological and hybrid physics–ML models, particularly for ingredient and formulation characterization, model‐based extrusion process control and optimization.