Determining the effective diffusivity coefficient and activation energy in thin-layer drying of Haj Kazemi peach slices and modeling drying kinetics using ANFIS

Majid Yazdani Barforoosh, Ali Mohammad Borghaee, Shahin Rafiee, Saeid Minaei and Babak Beheshti

International Journal of Low-Carbon Technologies, 2024, vol. 19, 192-206

Abstract: This study investigated the moisture changes in Haj Kazemi peach slices during drying in a thin-layer dryer at five temperature levels (40, 50, 60, 70, and 80°C), three levels of inlet air velocity (1, 1.5, and 2 m/s), and three slice thicknesses (2, 4, and 6 mm). The relative moisture content during drying was calculated, and an adaptive neuro fuzzy inference system (ANFIS) was used to predict the drying process of peach slices. The results indicated that slice thickness had a greater impact on drying time than air velocity. Moreover, an almost direct relationship was observed between changes in slice thickness and drying time. The effective moisture diffusivity coefficient in peach slices increased with an increase in slice thickness, temperature, and air velocity and ranged from 9.57 × 10^-10 to 4.33 × 10^-9 m^2/s under different experimental conditions. The calculated activation energy for drying peach slices under experimental conditions ranged from 16.74 to 20.48 kJ/mol. The designed model for simulating the drying conditions was based on an adaptive neuro fuzzy inference system (ANFIS) with input and output membership functions of triangular and linear shapes and a hybrid learning algorithm. The model could simulate the drying process with a correlation coefficient of 0.979.

Keywords: Peach; Drying; Thin-layer; Activation energy; Effective moisture diffusivity coefficient; Adaptive neural fuzzy inference system (ANFIS) (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:oup:ijlctc:v:19:y:2024:i::p:192-206.

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