No abstract

No abstract

No abstract

An air dryer was used for thin layer drying process of coroba, and moisture ratio at any drying time was compared by eight models (Newton, Page, Henderson and Pabis, logarithmic, two‐term, two‐term exponential, Verma et al., and Midilli and Kucuk). The effect of drying air temperature (71, 82 and 93C) and velocity (0.82, 1.00 and 1.18 m/s) on the coefficients of the best suited moisture ratio model was determined by multiple regression method. Coefficient of determination (R2) and reduced chi‐square (χ2) were used for the determination of the best suitable model. Among the mathematical models investigated, the Midilli and Kucuk, and logarithmic models satisfactorily described the drying behavior of coroba slices with highest R2 values and lowest χ2. The relationships between the constants k of the Midilli and Kucuk, and logarithmic models, with the drying variables of drying air temperature and velocity, were determined. PRACTICAL APPLICATIONS As a result, this work is intended to provide a significant tool for predicting the moisture content of the product at any time of the drying process, establish the air drying conditions in order to achieve given moisture content of coroba slices and optimize the design of the drying process.

The moisture mass transfer parameters characterizing the air drying of coroba slices were determined using the correlation between Biot and Dincet numbers. The air drying was carried out at temperatures of 71, 82 and 93C and velocities of 0.82, 1.00 and 1.18 m/s. Experimental moisture content data for coroba slices were collected. The drying coefficient and lag factor were calculated from the experimental data and were incorporated into the correlation. The moisture diffusion coefficient, Biot number and mass transfer coefficient ranged between 1.147 × 10−12–3.740 × 10−12 m2/s, 0.097–0.114 and 0.903 × 10−4–1.729 × 10−4 m/s, respectively. The predicted dimensionless moisture content profiles showed adequate agreement with the experimental observations, with the mean relative error between 0.98 and 4.61%. PRACTICAL APPLICATION As drying is an energy‐intensive operation, it has become the prime concern of the researchers to optimize process conditions that lead to energy savings. Moisture transfer parameters are important transport properties needed for accurate modeling in food drying applications. Therefore, accurate determination of these parameters for the drying operation is essential. There is a large amount of studies available in the literature to determine and calculate these parameters for the products subjected to drying. But limited studies have been carried out to determine these parameters using the drying process parameters in terms of lag factor and drying coefficient as first introduced by Dincer and Dost.