Recent advances in multiscale CFD modelling of cooling processes and systems for the agrifood industry

Application of computational fluid dynamics simulations in food industry

Computational fluid dynamics (CFD) is a tool for modelling and simulating processes in many industries. It is usually used as a choice to solve problem involving flow of fluids, heat transfer, mass transfer and chemical reaction. Moreover, it has also found application in the optimization of processes in branches of the food industry, including bread baking, cooling beef roast, or spray drying. CFD has enormous potential and many opportunities to improve the quality and safety of food products, as well as to reduce the costs of production and the use of machines and production equipment. In addition, empirical models only permit data to be extracted at a limited number of locations in the system (where sensors and gauges are placed). CFD allows the designer to examine any location in the region of interest, and interpret its performance through a set of thermal and flow parameters. Computer simulations are the future of every field of science, and the presented overview provides the latest information on experts and experiences related to CFD application in food production. Despite some disadvantages, such as the need to have a large reserve of computing power, the development of digital and IT technologies will make this problem insignificant in the nearest future. Then the CFD will become an indispensable element in the design of equipment and technological lines in the food industry.

Design and evaluation of a prototype solar energy driven onion curing system using CFD modeling

In developing countries like Ethiopia, where the time of harvesting coincides with the dry season, farmers cure onion bulbs naturally on the field. Field curing generally takes longer than artificial curing and results in more losses and reduced quality of the bulbs due to increased risks for infestations and uncontrolled suboptimal drying conditions. Large-scale artificial curing systems are expensive and electrical energy supply is limited in rural areas. A CFD model was employed to design an alternative and sustainable onion curing system that can be deployed on the field in rural areas. The developed CFD model was validated by comparing the predicted air velocity, temperature, and mass loss to measured values on a prototype curing system operated on a field in Ethiopia. A good agreement between the model and experimental value was observed for the time profiles of temperature at different positions in the bulk of onions during curing, expressed by a root mean square error of 1.1°C in the temperature range from 28 to 47°C, 0.16 m s−1 in the velocity range from 0.1 to 2.5 m s−1, and 0.565% for the mass loss that ranged up to 6.35%. The developed model was used to assess the air velocity, temperature, and relative humidity distribution in order to get an insight into the uniformity of curing of onion bulbs using the develop alternative curing system. For all of the examined curing durations, the drying air temperature variation inside 80% of the porous medium was < 3°C. In the remaining 20% of the porous medium, a temperature variation of up to 6°C was observed. Thus, the newly designed and developed curing system was found to cure the onion bulbs uniformly. Moreover, its performance was evaluated experimentally and the onions were cured to a desirable level of curing for long-term storage within a total curing duration of 48 h. It is vital to consider bulb shrinkage, particularly in the neck, in order to further improve the model mass loss prediction.

Sustainable food cold chain logistics: From microenvironmental monitoring to global impact

Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low-temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade-off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon. Industry empowerment through next-generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real-time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low-carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development.

Microstructural Classification and Reconstruction of the Computational Geometry of Steamed Bread Using Descriptor-Based Approach

Abstract

Microstructures affect the properties of food products; accurate and relatively less complex microstructural representations are thus needed for modelling of transport phenomena during food processing. Hence, the present study aimed at developing computational microstructures of steamed bread using descriptor-based approach. Relevant information was extracted from the scanning electron microscope (SEM) images of the steamed bread and evaluated using seven classifiers. For the automatic classification and using all descriptors, bagged trees ensembles (BTE) had the highest accuracy of 98.40%, while Gaussian Naïve Bayes was the least with 92.10% accuracy. In the “step forward” analysis, five descriptors had higher classification accuracy (98.80%) than all descriptors, implying that increase in descriptors might or might not increase classification accuracy. Microstructural validation revealed that the ellipse fitting method with a p value of 0.7984 for the area was found to be superior to the Voronoi method with a corresponding p value of 1.4554 × 10−5, confirming that the ellipse developed microstructure was more suitable for microscale modelling of transport phenomena in steamed bread.