Finite Element Method for Freezing and Thawing Industrial Food Processes

有限元法 工艺工程 食品工业 传质 传热 过程(计算) 计算机科学 冰点 机械工程 环境科学 生化工程 化学 工程类 机械 食品科学 热力学 结构工程 物理 色谱法 操作系统
作者
Tobi Fadiji,Seyed-Hassan Miraei Ashtiani,Daniel I. Onwude,Zhiguo Li,Umezuruike Linus Opara
出处
期刊:Foods [Multidisciplinary Digital Publishing Institute]
卷期号:10 (4): 869-869 被引量:29
标识
DOI:10.3390/foods10040869
摘要

Freezing is a well-established preservation method used to maintain the freshness of perishable food products during storage, transportation and retail distribution; however, food freezing is a complex process involving simultaneous heat and mass transfer and a progression of physical and chemical changes. This could affect the quality of the frozen product and increase the percentage of drip loss (loss in flavor and sensory properties) during thawing. Numerical modeling can be used to monitor and control quality changes during the freezing and thawing processes. This technique provides accurate predictions and visual information that could greatly improve quality control and be used to develop advanced cold storage and transport technologies. Finite element modeling (FEM) has become a widely applied numerical tool in industrial food applications, particularly in freezing and thawing processes. We review the recent studies on applying FEM in the food industry, emphasizing the freezing and thawing processes. Challenges and problems in these two main parts of the food industry are also discussed. To control ice crystallization and avoid cellular structure damage during freezing, including physicochemical and microbiological changes occurring during thawing, both traditional and novel technologies applied to freezing and thawing need to be optimized. Mere experimental designs cannot elucidate the optimum freezing, frozen storage, and thawing conditions. Moreover, these experimental procedures can be expensive and time-consuming. This review demonstrates that the FEM technique helps solve mass and heat transfer equations for any geometry and boundary conditions. This study offers promising insight into the use of FEM for the accurate prediction of key information pertaining to food processes.

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