Numerical Modeling of Ohmic Heating of Heterogeneous Food

Ohmic heating is a promising thermal processing technique in the food industry, as it offers rapid, uniform heating by passing an electrical current directly through the food product. However, food materials often exhibit heterogeneous structures—composed of regions with different conductivities, varied moisture levels, and complex geometrical features—which can result in non-uniform temperature distributions and pose challenges for process control. This study aims to develop and validate a comprehensive numerical modelling approach to predict the heating behaviour in heterogeneous food systems subjected to ohmic heating. A coupled multi-physics framework is used to simulate both the electrical and thermal phenomena. The governing equations for electric field distribution (based on current continuity and Ohm’s law) and transient heat transfer (using the standard heat conduction equation) are solved simultaneously. Temperature-dependent electrical conductivity and heat capacity are incorporated to capture the dynamic variations of material properties during the heating process. Additionally, geometric complexities—including solid particles, convection effects and varying product matrices—are represented in the simulation to emulate real-world food products. The numerical results highlight the formation of localized hotspots in regions with higher electrical conductivity, as well as cooler zones in lower-conductivity regions. Numerical analyses reveal that factors such as voltage gradient, particle size and distribution, and flow behaviour (if any) can significantly influence the final temperature profiles. Validation against experimental data demonstrates good agreement, suggesting that the proposed modelling approach can serve as a reliable tool for process design and optimization. This work provides valuable insights into how heterogeneity affects thermal treatment outcomes and offers guidance for developing robust ohmic heating modelling to support efficient process scale-up.