Kinetic and artificial neural network modeling techniques to predict the drying kinetics of Mentha spicata L.

Abstract

This study presented both the empirical and artificial neural network (ANN) approaches to estimate the moisture content of Mentha spicata. Two different types of drying methods (in shade and in oven (35 and 50°C)) were used to investigate the drying kinetics of the Mentha spicata samples. The effects of drying methods on effective diffusion coefficient, moisture ratio (MR), drying rate, and activation energy were investigated. Moreover, six different thin layer drying models (Page, Diffusion approach, Newton, Modified Henderson, Henderson and Pabis and Pabis and Midilli) and an ANN with feed forward structure were used to define the drying kinetics of these samples. In order to estimate the kinetic model parameters, sequential quadratic programming (SQP) was used. Model performances were evaluated based on the coefficient of determination (R2), root mean square error (RMSE) and mean absolute percentage error (MAPE%) values. In the kinetic part of the modeling study, the Midilli model provided better results than the others. However, the ANN had the best results when a total assessment was made. The effective diffusion coefficient values were found in the range between 1.31 × 10–12 and 4.43 × 10–12 m2/s. The activation energy was obtained as 44.31 kJ/kmol. The R2, MAPE%, and RMSE values for the ANN test data were 1.00, 0.2257, and 5.9447 × 10−4, respectively. In the future, different modeling approaches will be applied to describe this drying process. Practical applications Drying is a process where heat transfer and mass transfer take place together. Modeling is an innovative approach used in evaluation of experimental data and has increasing popularity in recent years. ANNs are a powerful data‐driven method, and they have a very broad area of usage from medicine to engineering issues. Empirical models are another approach for describing experimental data. In this study, these two modeling approaches were used to obtain the MR. Humidity is a condition that needs to be checked in food safety and protection. Therefore, it is very important to ensure control with robust modeling techniques. In this study, the developed ANN model had a high R2 value (R2 = 1.00). This indicated that it may be used successfully in real applications.

Mathematical and intelligent modeling of stevia (Stevia Rebaudiana) leaves drying in an infrared‐assisted continuous hybrid solar dryer

“…Higher MR prediction accuracy of ANNs compared to mathematical models was also reported by Karakaplan et al. (2019) for estimating the MR of spearmint during drying process, and by Omid et al. (2009) for modeling the drying kinetics of pistachio nuts.…”

Parboiled Paddy Drying with Different Dryers: Thermodynamic and Quality Properties, Mathematical Modeling Using ANNs Assessment

“…As a result, it produces an output [19]. Some researchers have recently focused on the ANN and mathematical models in drying kinetics of agriculture products, such as eggplant [16], quince slices [20], sour cherry [21], Mentha spicata L. [22].…”

Thin-layer drying model of Cosmos caudatus

“…However, different drying methods, either using a thermal convection oven or a hot air dryer, also indicated an increase in the Deff coefficient when the temperature was increased [13,15,22]. This is most likely because high temperature increases heat absorption in a material that increases mass transfer and drying speed [26,28]. Hence, it can be seen that the Deff coefficient highly depends on the increase in the drying temperature.…”

“…Figure 1 Figure 2 The relation in terms of drying speeds and drying temperatures was shown in Figure 3.. Significantly, the drying rate increased at an elevated drying temperature, resulting in a rapid depletion of moisture content [15,22,26]. On the other hand, the higher drying rate accelerates further heat transfer and causes rapid removal of moisture from the material.…”

“…The determination of the effective moisture diffusion coefficient during the drying process of C. caudatus was estimated using Fick's second law. The equation is expressed by Equation ( 8) [22,26] :…”