Artificial Intelligence Techniques for Food Drying Technology
Applications of artificial intelligence techniques, such as artificial neural networks, fuzzy logic, genetic algorithms and neural-fuzzy systems, in engineering have gained momentum in past decade. Main applications of these techniques in engineering are estimation, optimization and control process. In this paper, some of the applications are studied and both simulation and real-time experimental results are given. Artificial neural networks and genetic algorithms are very useful for estimation and optimization process for drying technologies. However, fuzzy logic is also capable of both classification and control of the drying process. Estimation, optimization and control applications of artificial intelligence methods are given in detail for different types of food drying applications. Echinacea angustifolia and carrot are selected as application examples. A fuzzy logic based control approach is employed to control a convective type drier. Estimation and optimization applications of artificial neural networks and genetic algorithms are compared with non-linear regression analysis. In addition, fuzzy control is also compared with a classical control technique to conclude the robustness of the fuzzy control in terms of classical control. According to the results, it is observed that artificial intelligence techniques have several advantages such as: decreasing computation time, increasing stability and accuracy. Moreover these techniques could be applicable for different type processes with simple changes in configuration.
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