Review on the recent trends of food dryer technologies and optimization methods of drying parameters

Dawit Andualem Asrate, Addisu Negash Ali
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Abstract

In food drying processes, inadequate control of drying conditions can lead to under- or over-dried foods, often due to uncontrolled parameters including dryer type, airflow distribution, and optimal drying conditions. This review examines common food drying technologies, methods for optimizing the drying parameters, and methods for model selection for accurate data fitting. The drying methods covered include open sun drying, solar drying, cabinet drying, drum drying, spray drying, freeze drying, fluidized-bed drying, potted-bed drying, superheated steam drying, and microwave drying. Furthermore, tools with better optimization and prediction capabilities for various food types such as Computational Fluid Dynamics (CFD), ANSYS Multi-Objective Genetic Algorithm (MOGA), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Genetic Algorithm (GA)-tuned Artificial Neural Networks (ANN), Analysis of Variance (ANOVA), and Response Surface Methodology (RSM) are reviewed in detail. These tools offer valuable methods for determining optimal drying parameters across different drying technologies and food products. Additionally, theoretical, semi-empirical, and empirical thin-layer models are discussed as effective methods for accurately fitting drying process data. Achieving high-quality dried products requires maintaining uniform airflow distribution within the dryer and optimizing key parameters such as drying temperature, moisture content, drying rate, drying time, and airflow speed. One of the primary challenges in existing drying technologies is the non-uniform airflow distribution throughout the drying chamber, which directly affects both quality and efficiency of the drying process. The future of food drying technology has focused on advancing the processing methods, and integrating and employing hybrid drying methods to enhance drying efficiency.
综述了食品干燥技术的最新发展趋势及干燥参数的优化方法
在食品干燥过程中,干燥条件控制不当可能导致食品干燥不足或干燥过度,这通常是由于不受控制的参数,包括干燥机类型、气流分布和最佳干燥条件。本文综述了常见的食品干燥技术,优化干燥参数的方法,以及精确数据拟合的模型选择方法。所涉及的干燥方法包括开放式日光干燥、太阳能干燥、柜式干燥、滚筒干燥、喷雾干燥、冷冻干燥、流化床干燥、罐床干燥、过热蒸汽干燥和微波干燥。此外,对计算流体动力学(CFD)、ANSYS多目标遗传算法(MOGA)、自适应神经模糊推理系统(ANFIS)、遗传算法(GA)调谐人工神经网络(ANN)、方差分析(ANOVA)和响应面方法(RSM)等具有较好优化和预测能力的工具进行了详细综述。这些工具为确定不同干燥技术和食品的最佳干燥参数提供了有价值的方法。此外,理论、半经验和经验薄层模型是准确拟合干燥过程数据的有效方法。要获得高质量的干燥产品,需要保持干燥机内气流分布均匀,并优化干燥温度、含水率、干燥速度、干燥时间和气流速度等关键参数。现有干燥技术面临的主要挑战之一是干燥室内气流分布不均匀,直接影响干燥过程的质量和效率。食品干燥技术的未来发展方向是改进加工方法,整合和采用混合干燥方法来提高干燥效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
4.50
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