Mathematical Modeling for Virtualization in Food Processing

Abstract

The design strategy of a food process must be aimed to provide food safety while retaining optimal organoleptic and nutritional characteristics and, possibly, to optimize the energy consumption. As a matter of fact, it represents a challenge in food process engineering. In this framework, it is essential to determine the interactions among transport phenomena (mass, heat, and momentum) and any other relevant physics for further optimal design and for driving possible innovation. In other technological sectors (like automotive or aerospace industries), virtualization and mathematical modeling are standard methods used for optimal design, while in food process engineering the contribution of such tools has not been fully exploited. Since virtualization represents a new and sophisticated strategic tool to design and to innovate a process, the objective of this review was to introduce virtualization and mathematical modeling in the food processing industry. For this purpose, motivation and needs for virtualization in food processing were focused, mathematical modeling background and various approaches for modeling were introduced, and solution methods and required initial–boundary conditions with thermal-physical properties were outlined. Also complexity, computational cost, and model validation techniques were critically discussed. Virtualization and mathematical modeling dominate the major requirements to design, optimize, and innovate food processing with their vast opportunities and potentials and they will become a cornerstone of utmost importance to food engineering domain

Finite Element Method for Freezing and Thawing Industrial Food Processes

“…Models based on observation begin with experimental data to build the model and are empirical in nature. The physics-based models depend on universal physical laws to describe the physical phenomena of interest and are preferred for predictive models in many areas, including food preservation and processing [ 33 , 34 , 35 ]. Numerical or mathematical modeling is extensively used in many areas to analyze or improve an existing process and design and develop new concepts.…”

“…Besides, solving mass transfer equations is crucial in modeling the movement of moisture in food products [ 42 ]. Additionally, in a process where the textural properties, sensory attributes, and destruction of nutrients or pathogenic microorganisms is of interest in modeling, reaction kinetics and structural mechanics would play a crucial role [ 33 ]. Complex processes can involve several equations for different variables of interest that could be coupled [ 38 ].…”

“…Food preservation and processing methods involving structural and physical properties and transport phenomena introduce an additional degree of complexity with processing constraints related to the functional, nutritional and organoleptic properties [ 33 ]. Over the years, the development of food processing technologies and strategies has been modeled to understand the different complex mechanisms involved in food processing.…”

Application of Constrained Optimization Techniques in Optimal Shape Design of a Freezer to Dosing Line Splitter for Ice Cream Production

“…On the other hand, computational science and engineering techniques [1] have allowed a major change in the way that products and equipment can be engineered, as a computational model that simulates the physical processes can be built rather than building real-world prototypes and performing experiments. Among such techniques, optimal shape design (OSD) [2] represents an interesting approach.…”

“…In food industry, optimum design is not a once and for all solution tool because engineering design is made of compromises owing to the multidisciplinary aspects of the problems and the necessity of doing multipoint constrained design [1]. From a mathematical point of view, OSD is a branch of differentiable optimization and more precisely the application of optimal control for distributed systems [11], where gradient and Newton-based methods are the natural numerical tools.…”