Magnetrons are the most common microwave power sources. However, variation of output frequencies of magnetrons makes heating patterns of foods in multi-mode microwave cavities random and unpredictable. It is desirable to find effective alternatives, such as solid-state microwave generators. Here, we compared the frequency spectra of magnetrons and solid-state generators to evaluate their influence on the heating performance of microwave ovens. Results showed that the spectrum (i.e., peak frequency and bandwidth) of microwaves from the magnetron varied depending on the food and food position and varied considerably between individual ovens of the same model. In contrast, the solid-state microwave generator provided microwaves not only at exactly the set frequency but also within a narrow band, regardless of food loads. That is, solid-state generators had better spectral quality than that from magnetrons. As a result, solid-state generators would provide predictable and stable heating patterns of foods that cannot be achieved with magnetrons. Therefore, solid-state generators create new opportunities in designing next-generation microwave systems with high heating performance. Heating patterns of foods in magnetron-powered multi-mode cavities are random and unpredictable, limiting the use of microwave heating to address food safety issues. Solid-state microwave generators have the potential to overcome this drawback by providing better spectral quality than magnetrons, as shown in this study. In addition, the methodology developed in this work to measure the frequency spectrum of microwave generators is helpful in improving the accuracy of computer simulations. This study provides fundamental information and useful guidance to the food industry on designing and mathematically modelling solid-state powered microwave heating systems. 

Heating performance of microwave ovens powered by magnetron and solid-state generators

Effect of lossy thin-walled cylindrical food containers on microwave heating performance

Microwave drying (MWD) is an efficient dielectric drying method in food, with advantages such as volumetric heating, fast drying, safety, and good product quality. As a key indicator of a dryer's market value, energy efficiency is of concern to sellers and dryer manufacturers. This paper systematically reviewed the quantification methods and influencing factors of energy efficiency of microwave drying in food application from different perspectives. Mechanisms and possible improvements of these factors are highlighted. Future trends in improving the energy efficiency of MWD are proposed. Energy consumption of MWD depends on a variety of factors such as equipment structure, drying conditions (microwave power, frequency, temperature, and air velocity), material properties, and combined/hybrid drying technologies. The drying system can be effectively improved if these parameters are adjusted appropriately and taking the processing cost into consideration. Although a good product can be obtained by pretreatment or combined/hybrid drying method, it may consume more energy. Future research should develop artificial intelligence, renewable energy, and computational fluid dynamics technology to pave the way for large-scale application of MWD and reduce energy consumption.

Factors affecting energy efficiency of microwave drying of foods: an updated understanding.

Development of online closed-loop frequency shifting strategies to improve heating performance of foods in a solid-state microwave system.

Steam blanching strengthened far-infrared drying of broccoli: Effects on drying kinetics, microstructure, moisture migration, and quality attributes

The microwave-assisted thermal process is a high-efficiency drying method and is promising to be applied in the food industry. However, the prediction of the thermal treatment results from such a dynamic and complicated process can be difficult. Additionally, the determination of the optimal drying parameters, such as drying temperature, microwave power, and drying time for optimized performance can also be hard. Recently, extensive research has been focusing on the use of Artificial Neural Network (ANN) models in the lab-scale microwave drying processes and has shown the feasibility of such application. As a regression tool, the ANN models have been widely used in predicting drying performance; when integrated with additional optimizing algorithms, the ANN models could be used for drying parameter optimization; and when combined with real-time measuring techniques (e.g., nuclear magnetic resonance), the ANN models could be used for monitoring and controlling the drying process in a dynamic sense. Future research could focus on testing the developed ANN models in industrial-scale microwave drying processes and applying the ANN models in microwave drying kinetics research for optimizing the dynamic drying processes. This article is protected by copyright. All rights reserved.

Recent Application of Artificial Neural Network in Microwave Drying of Foods: A mini-review.

Nonuniform microwave heating causes localized over-heating in frozen food products, lowering the quality of the thawed products. The solid-state microwave system has the potential to enhance heating performance as it provides flexible control over microwave parameters, e.g., frequency and power. The real-time collected microwave power reflection enables dynamic parameter control. This study developed two dynamic solid-state defrosting strategies: 1) selective frequency shifting strategy that only selectively shifted frequencies with high efficiency, 2) adaptive power control strategy that adapted power input with simultaneously orderly shifted frequencies. Results showed that the adaptive power control strategy had a better defrosting performance on frozen mashed potato and pork chop samples. Besides, with the adaptive power control strategy, the rotation of the turntable did not pose a significant improvement on the standard deviation of the temperature distribution in the defrosted products when compared to the stationary heating, which might be due to the minimized movement of samples at the center of the. The absolute changes in temperature profiles contributed by the rotation were considerable, indicating the importance of turntable. This study developed novel microwave control strategies for defrosting processes, taking the advantage of the flexible control over the microwave parameters provided by the solid-state technique. With the proposed strategy, the microwave defrosting performance can be significantly improved in comparison with the magnetron-based microwave oven performance. The implemented dynamic strategy is promising to be embedded in the algorithm for use by the future smart microwave ovens. 

Dynamic solid-state microwave defrosting strategy with shifting frequency and adaptive power improves thawing performance

A Comprehensive Evaluation of Microwave Reheating Performance Using Dynamic Complementary-Frequency Shifting Strategy in a Solid-State System

Computational Fluid Dynamics (CFD) is a valuable tool for studying fluid environments within cell culture bioreactors and optimizing processing parameters, but it can be computationally expensive. This study developed an artificial neural network (ANN)-based machine learning model to predict and correct the coarse-mesh-induced errors in CFD modeling of a spinner flask bioreactor. A baseline ANN model was trained to predict the velocity error function between the coarse and optimized reference mesh results at one rotational speed (90 rpm), demonstrating that the ANN-based approach could correct the coarse-mesh velocity with RMSE values of nodal velocities improved by an average of ∼20% at different rotational speeds. The effect of ANN structure, input data normalization, and training dataset combinations on prediction performance was evaluated. More neurons and hidden layers generated better results but required more computational time for training. The model's generalization capabilities were further evaluated in case studies of correcting velocity and Kolmogorov length at different fluid viscosity and bioreactor impeller geometry conditions. Results suggested that the ANN model had better generalization in correcting Kolmogorov length than velocity. This research provides insights into using a machine learning approach to enhance CFD modeling in bioreactor applications, contributing to advancing tissue engineering processes. 

An artificial neural network-based machine learning approach to correct coarse-mesh-induced error in computational fluid dynamics modeling of cell culture bioreactor

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