Mathematical modeling of transport phenomena for simulation and optimization of food processing operations

In a simulation study, a process is imitated by using mathematical models as a quick way of studying and evaluating the process for further design and optimization purposes. Simulations are carried out by using mathematical models with required assumptions for system inputs to determine the outputs. Hence, simulation of a process is based on availability of a computational model. Mathematical models can be observation (depending upon the availability of experimental data) or physics based (with formulation of the transport phenomena governing the process). Empirical observational models are limited for their predictive capabilities, but they might be useful and effective when the system is well defined with experimental data. However, `mathematical modeling` term, in a more common sense, should be used for physics based computational models due to their predictive capabilities for various situations. Food process modeling is an interdisciplinary approach that involves engineering approaches for transport phenomena driving the process with chemistry, reaction kinetics and predictive microbiology. Essential aspects of a food process model are to define any physical, chemical or biological changes, to develop mathematical basis with appropriate assumptions, to solve the problem with required mathematical background and to validate the model for various conditions. Predictions from simulations can be then used for further design and optimization purposes replacing. In this study, mathematical model approaches for simulation of food processing operations were summarized focusing on heat transfer and fluid dynamics which are encountered widely in different food processing operations. For this purpose, analytical solutions, numerical approaches and CFD methodologies bringing new innovative approaches into the modeling were described briefly with examples from the literature. In addition, significance of model validation studies and optimization approaches were explained with traditional and innovative techniques.