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Drying kinetic and artificial neural network modeling of mushroom drying process in microwave‐hot air dryer

Omari, Amin, Behroozi‐Khazaei, Nasser, Sharifian, Faroogh
Journal of food process engineering 2018 v.41 no.7 pp. e12849
air, air temperature, color, dynamic models, energy, heat, industry, microwave drying, mushrooms, neural networks, prediction, product quality, specific energy, water content
Modeling of moisture content variation under variable microwave power in microwave‐hot air dryer is challenging. In this study, one static and one dynamic of ANN were investigated for modeling of whole mushroom drying process. The experiments were done in three levels of hot air temperature (23, 50, and 70 °C), three levels of microwave power density (MPD) (1.5, 2, and 2.5 W/g) and two statuses of microwave power during drying process (constant and variable). The results demonstrated that the two distinct falling rate periods were observed at low MPD with any air temperatures and power density status. The lowest specific energy consumption was found in 23 °C and 2.5 fixed MPD and lowest color deterioration found in 70 °C and 1.5 fixed MPD. The results also showed the 3–6–7–1 structure of dynamic model with 0.2179 RMSE and 0.9914 R values had better results than other structures and would be very suitable in a predictive control system for drying control. PRACTICAL APPLICATIONS: Volumetric and high efficiency heating are the primary benefit of microwave drying and lot of industries and producers interest to use it. Also, modeling of drying kinetics and evaluating of product quality in each drying method provide valuable information to improve their drawbacks and recommend it for industry. Application of microwave technology in dehydration of mushroom can result in significant improvement in quality and energy consumption. On the other hand investigating a robust ANN model for predicting the moisture content helps to better prediction and control of microwave‐hot air dryer in industrial scale.