Mathematical modelling of lemon verbena leaves drying in a continuous flow dryer equipped with a solar pre-heating system

S. Soodmand-Moghaddam; M. Sharifi; H. Zareiforoush; H. Mobli

doi:10.15586/QAS2019.658

S. Soodmand-Moghaddam Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.
M. Sharifi Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.
H. Zareiforoush Department of Mechanization Engineering, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran.
H. Mobli Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran.

Keywords

air velocity, air temperatures, drying kinetic, Midilli and Kucuk’s model

Abstract

The modelling of the drying process of lemon verbena leaves in a continuous flow dryer equipped with a solar pre-heating system was performed at three levels of drying temperature (50, 40, and 30 °C) and three levels of air velocity (2, 1.5 and 1 m/s). During the experiments, lemon verbena leaves were dried to the final moisture content of 10 from 76% wet basis in the continuous flow dryer. Drying kinetic showed to drying temperature and air velocity exerted significant influence on the drying time. Also, the dried lemon verbena leaves quality was obtained by determining the essential oil content of the product after drying in different conditions in the dryer. Finally, it was observed that the highest essential oil content was maintained at a temperature of 40 °C and air velocity of 1 m/s. Suitability of 10 different mathematical drying models was used to describe drying lemon verbena leaves in this dryer. The results have shown that Midilli and Kucuk’s model can successfully predict the experimental data in all air temperatures and air velocities. In Midilli and Kucuk’s model, the amounts of R² were above 999 × 10^–3 and the amounts of root-mean-square error (RMSE) and chi-square (χ²) were less than 174 × 10^–4 and 19 × 10^–4.

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