Abstract

Drying operations can help in reducing the moisture content of food materials for avoidance of microbial growth and deterioration, for shelf life elongation, to minimize packaging and improving storage for easy transportation. Thin-layer drying of materials is necessary to understand the fundamental transport mechanism and a prerequisite to successfully simulate or scale up the whole process for optimization or control of the operating conditions. Researchers have shown that to rely solely on experimental drying practices without mathematical considerations for the drying kinetics, can significantly affect the efficiency of dryers, increase the cost of production, and reduce the quality of the dried product. An effective model is necessary for the process design, optimization, energy integration and control; hence, the use of mathematical models in finding the drying kinetics of agricultural products is very important. The statistical criteria in use for the evaluation of the best model(s) has it that coefficient of determination (R2) has to be close to unity while the rest statistical measures will have values tending to zero. In this work, the essence of drying using thin-layer, general approaches to modeling for food drying mechanisms thin layer drying models and optimization of the drying processes have been discussed.

Highlights

  • The removal of moisture can be due to simultaneous heat and mass transfer [1]

  • The quality of the fitted models are evaluated with the following statistical measures: Correlation coefficient (r or R), coefficient of determination (r2 or R2), Reduced chi square (χ2), mean bias error (MBE), root mean square error (RMSE), Sum square error (SSE), mean relative error root square (RRMS), modelling efficiency (EF), mean percent error (MPE), Mean square error (MSE)

  • Artificial Neural Networks (ANN) is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students [116]

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Summary

Introduction

The removal of moisture can be due to simultaneous heat and mass transfer [1]. Purposely it is carried out to reduce water to the level at which microbial spoilage and deterioration reactions are greatly minimized [2]. The processes involved are mainly (bio) chemical and physical reactions These changes proceed at a certain rate and with certain kinetics. Drying kinetics is used to express the moisture removal process and its relation to the process variables and a good understanding of the drying rate is important to develop a drying model [7]. Despite the essence of kinetics, modeling of particulate or thin-layer drying of materials is necessary to understand the fundamental transport mechanism and a prerequisite to successfully simulate or scale up the whole process for optimization or control of the operating conditions. Mathematical modeling of dehydration process is an inevitable part of design, development and optimization of a dryer [8] It mainly involves elaborative study of drying kinetics, which describes the mechanisms and the influence that certain process variables exert on moisture transfer [9].

Mechanism of Drying
Thin Layer Drying Process
Theoretical Method
Empirical Method
Semi-Empirical Method
Drying Constant
Derivation of Thin layer Models
Classification of Model
Lumped Element Model
Factors That Affects Drying Kinetics
Goodness of Fit Statistics
Optimization
10. Conclusion
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