About this Research Topic
The development and application of novel food processing technologies have gained significant attention in recent years due to their potential to support sustainability and net-zero development. Technologies such as Ohmic heating, high pressure, and ultrasound have been shown to improve the nutritional quality and acceptability of food products while reducing energy and water consumption. However, the proper design, implementation, and selection of process parameters remain an area requiring further development and knowledge. In addition, the transportation of granular materials, commonly encountered during food processing in equipment such as milling or husking machines, remains largely understudied. A deeper understanding of the behavior of these materials is crucial for optimal design and operation in food processing, making it a promising area for future research.
Modeling is a powerful tool in the design and optimization of real-world systems, representing the system through mathematical representation. The models generated from this exercise provide valuable information and physical insight into the process. These models can take the form of mathematical models, physical models, or hybrid models. Simulations utilizing these models allow for the computational evaluation and optimization of process conditions, making it possible to predict the effects of changes to key parameters such as temperature, electric field, pressure, rheology, environment, and boundary conditions. This capability reduces the cost of laboratory or pilot tests and provides realistic outcomes that would otherwise be unattainable. In essence, modeling and simulation offer a versatile and cost-effective way to understand and optimize complex real-world systems.
In an effort to address limitations posed by conventional technologies, new and innovative approaches are being developed to fill existing technological gaps. These novel technologies aim to achieve improved results in areas such as higher retention of bioactive compounds, better product quality, increased consumer acceptability, enhanced process control, increased energy efficiency, and reduced processing time. However, the variability of starting materials and process conditions can make it challenging to consistently produce high-quality products, presenting a significant obstacle in the implementation of these new technologies.
In advanced food processing technologies, the desired outcomes are often multifaceted and challenging to measure. As a result, modeling and simulation play a crucial role in the evaluation and optimization of these processes to ensure consistent and uniform results. These tools allow for a comprehensive examination of the impact of process conditions and parameters on the product without the need for extensive physical testing.
The application of numerical modeling in advanced food processing technologies enables us to effectively simulate complex real-world conditions. Techniques like Finite Element Methods (FEM) can handle the complexities of multi-phenomena in non-homogeneous media to model heat and mass transfer in Ohmic Heating processes. Simulations using particle-based dynamics, such as the Discrete Element Method (DEM), provide valuable insight into particle behavior at a microscopic level, however, a unified agreement between particle behavior and bulk flow behavior, as solved in continuum models, is still being pursued.
The aim of this topic is to encompass the advancement of mathematical and physical models and simulation techniques for emerging technologies, with the goal of creating a comprehensive platform for sharing and exchanging knowledge related to overcoming challenges in the scientific understanding, design, implementation, and selection of process parameters for these technologies.
The novel food processing technologies include, but are not limited to, the following advanced thermal, mild and nonthermal processing technologies based on the utilization of:
1) Mechanical Energy
Eg. Pressure (hydrostatic), Ultrasonication, Membrane based processing, micro and nanofluidic.
2) Electrical and Electromagnetic Energy
Eg. Irradiation and Light based process, Microwave and Radio Frequency heating, Electric Field Processing (MEF, PEF, Ohmic), and other advanced methods.
3) Chemical agents
Eg. Advanced oxidation (including ozone), Super critical processes.
4) Advance Drying technologies
Eg. Refractance window, Microwave drying.
5) Granular materials processing
Eg. DEM, CFD-DEM, SPH, 2D and 3D continuum modeling, husking, milling, rheology, and constitutive law.
6) Machine learning and deep learning
Eg. Data space analytics, surrogate model, statistical characteristics, and contact model.
Modeling is a powerful tool in the design and optimization of real-world systems, representing the system through mathematical representation. The models generated from this exercise provide valuable information and physical insight into the process. These models can take the form of mathematical models, physical models, or hybrid models. Simulations utilizing these models allow for the computational evaluation and optimization of process conditions, making it possible to predict the effects of changes to key parameters such as temperature, electric field, pressure, rheology, environment, and boundary conditions. This capability reduces the cost of laboratory or pilot tests and provides realistic outcomes that would otherwise be unattainable. In essence, modeling and simulation offer a versatile and cost-effective way to understand and optimize complex real-world systems.
In an effort to address limitations posed by conventional technologies, new and innovative approaches are being developed to fill existing technological gaps. These novel technologies aim to achieve improved results in areas such as higher retention of bioactive compounds, better product quality, increased consumer acceptability, enhanced process control, increased energy efficiency, and reduced processing time. However, the variability of starting materials and process conditions can make it challenging to consistently produce high-quality products, presenting a significant obstacle in the implementation of these new technologies.
In advanced food processing technologies, the desired outcomes are often multifaceted and challenging to measure. As a result, modeling and simulation play a crucial role in the evaluation and optimization of these processes to ensure consistent and uniform results. These tools allow for a comprehensive examination of the impact of process conditions and parameters on the product without the need for extensive physical testing.
The application of numerical modeling in advanced food processing technologies enables us to effectively simulate complex real-world conditions. Techniques like Finite Element Methods (FEM) can handle the complexities of multi-phenomena in non-homogeneous media to model heat and mass transfer in Ohmic Heating processes. Simulations using particle-based dynamics, such as the Discrete Element Method (DEM), provide valuable insight into particle behavior at a microscopic level, however, a unified agreement between particle behavior and bulk flow behavior, as solved in continuum models, is still being pursued.
The aim of this topic is to encompass the advancement of mathematical and physical models and simulation techniques for emerging technologies, with the goal of creating a comprehensive platform for sharing and exchanging knowledge related to overcoming challenges in the scientific understanding, design, implementation, and selection of process parameters for these technologies.
The novel food processing technologies include, but are not limited to, the following advanced thermal, mild and nonthermal processing technologies based on the utilization of:
1) Mechanical Energy
Eg. Pressure (hydrostatic), Ultrasonication, Membrane based processing, micro and nanofluidic.
2) Electrical and Electromagnetic Energy
Eg. Irradiation and Light based process, Microwave and Radio Frequency heating, Electric Field Processing (MEF, PEF, Ohmic), and other advanced methods.
3) Chemical agents
Eg. Advanced oxidation (including ozone), Super critical processes.
4) Advance Drying technologies
Eg. Refractance window, Microwave drying.
5) Granular materials processing
Eg. DEM, CFD-DEM, SPH, 2D and 3D continuum modeling, husking, milling, rheology, and constitutive law.
6) Machine learning and deep learning
Eg. Data space analytics, surrogate model, statistical characteristics, and contact model.
Keywords: Modeling, Simulation, Food processing, Sustainability, Ohmic heating, High pressure, Ultrasound, Machine learning, Drying technologies
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