Modeling Thin-Layer Drying of Turnip Slices Under Semi-Industrial Continuous Band Dryer

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In this study, different drying conditions were investigated on quality and thermodynamic properties of almond kernel. Experiments were performed using a convection dryer with ultrasound pretreatment in 40, 50, 60, and 70 °C air temperature, 1 m/s air velocity, and duration of ultrasonic pre‐treatment of 0 min (for control sample), 10, 20, and 40 min. The drying kinetic of the almond kernel was estimated by 15 mathematical models. Furthermore, Artificial Neural Networks (ANNs) and Adaptive Neuro‐Fuzzy Inference Systems (ANFIS) were applied to fit the experimental data on the thin layer drying. The lowest and highest values of the effective moisture diffusivity (Deff) was 1.81 × 10−9 and 9.70 × 10−9 m2/s, respectively. Activation energy (Ea) of the samples was obtained between 26.35 and 36.44 kJ/mol. The highest and lowest values of specific energy consumption (SEC) were calculated 561.72 and 169.88 kW hr/kg, respectively. Maximum (13.14%) and the minimum (7.1%) values of shrinkage were achieved at air temperatures of 70 and 40 °C, respectively. The color changing of dried samples was obtained between 9.14 and 17.96. Furthermore, results revealed that the ANFIS model had the high ability to predict the moisture ratio (R2 = 0.9998 and MSE = 0.0003) during drying. As a result, ANFIS model has the highest ability to evaluate all output as compared with other models and ANNs method. Practical applications Algorithms are modern methods that have been successfully applied to solve the various problems and modeling in engineering and science. Drying is one of the oldest procedures to preserve the food quality. Reduction of moisture content to a certain value can be caused to decay and minimize the microbiological activity and deteriorating chemical reactions in agricultural products, respectively. Determination of almond drying process under convective with ultrasound pre‐treatment dryer in terms of desirable thermal properties (effective moisture diffusivity and energy consumption) provides the high‐quality products. Furthermore, this research can be able to provide a technical basis for almond drying and the related equipment designing.

“…D eff for agricultural and food products can vary between 10 −12 and 10 −8 m 2 /s (Kaveh & Amiri Chayjan, ). The D eff values for this study are shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

The effect of ultrasound pre‐treatment on quality, drying, and thermodynamic attributes of almond kernel under convective dryer using ANNs and ANFIS network

Kaveh

Jahanbakhshi

Abbaspour‐Gilandeh

et al. 2018

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“…The total drying time was considerably decreased with rising air temperature. This decreasing can be because of the point that higher air temperature causes larger vapor pressure difference among surface and center of tarragon (Kaveh & Amiri Chayjan, ). Similar results were observed by Onwude, Hashim, Janius, Nawi, and Abdan () for drying of Pumpkin Slices, Santacatalina, Soriano, Cárcel, and Garcia‐Perez () for drying of eggplant and Junqueira, Correa, and Ernesto () for drying of cape gooseberry fruits.…”

Section: Resultsmentioning

confidence: 99%

“…Figure presents the amount of ( D eff ) of tarragon at different conditions achieved by Equation . Based on the literature, the amount of D eff is ranged within 10 −12 to 10 −8 m 2 /s for agriculture product using the drying methods (Kaveh & Amiri Chayjan, ). According to the results, the values of D eff for tarragon varied between 1.34 × 10 −10 to 2.74 × 10 −10 m 2 /s.…”

Section: Resultsmentioning

confidence: 99%

“…Diffusion is a principal mechanism during moisture movement to the surface which is vaporized (Salehi, Kashaninejad, & Jafarianlari, ). The second law of Fick's diffusion described the drying process during the decreasing rate period for more foodstuffs (Amiri Chayjan et al, ; Kaveh & Amiri Chayjan, ; Kayran & Doymaz, ). The second Fick's law for effective moisture diffusivity in thin layer drying was proposed as (Crank, ):

MR = \frac{(M - M_{normale})}{(M_{normali} - M_{normale})} = \frac{8}{{normalπ}^{2}} \sum_{n = 1}^{\infty} \frac{1}{{((), 2 n - 1)}^{2}} \exp ((), - {(2 n - 1)}^{2} π^{2} t \frac{D_{eff}}{4 L^{2}})

where, L is the half thin layer thickness (m) and t is the drying time (s).…”

Section: Methodsmentioning

confidence: 99%

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Using PSO and GWO techniques for prediction some drying properties of tarragon (Artemisia dracunculus L.)

Karami

Kaveh

Mirzaee‐Ghaleh

et al. 2018

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In this article, the effects of air temperature and velocity on drying characteristics of tarragon (Artemisia dracunculus L.) were investigated. The experiments were done at four temperatures of 40, 50, 60, and 70 °C and three air velocities of 1, 1.5, and 2 m/s. According to the results, the drying term of tarragon reduced significantly with increasing drying air temperature. The values of effective moisture diffusivity (Deff) were ranged between 1.34 × 10−10 and 2.74 × 10−10 m2/s. Also, by increasing drying air temperature the values of specific energy consumption (SEC) were decreased. The values of SEC were between 20.50 and 66.90 MJ/kg. Also Deff and SEC values were modeled by particle swarm optimizer (PSO) and gray wolf optimizer (GWO) algorithms. Drying air velocity and air temperature were considered as input parameters for the models. Based on three statistical parameters includes R2, MSE, and MAE for predicting Deff and SEC, GWO performance was better than the PSO. Practical applications The purpose of drying crops is decreasing of moisture content for providing secure storage and significant influence on the quality of dried crops. In this research, the effective moisture diffusivity (Deff) and specific energy consumption (SEC) values were modeled by PSO and GWO algorithms. The procedure can be applied in postharvest processing of medicinal plants.

“…The ANN can establish a functional relationship between input and output variables by training and learning (Chojaczyk, Teixeira, Neves, Cardoso, & Guedes Soares, ). In recent years, ANN models have been studied for the modeling of drying of potato cubes (Azadbakht, Aghili, Ziaratban, & Torshizi, ), bay leaves (Aktaş, Şevik, Özdemir, & Gönen, ), mango ginger (Murthy & Manohar, ), sour cherry (Motavali, Najafi, Abbasi, Minaei, & Ghaderi, ), onion (Jafari, Ganje, Dehnad, & Ghanbari, ), kiwi (Mahjoorian et al, ), zucchini (Tavakolipour, Mokhtarian, & Kalbasi‐Ashtari, ), green bell pepper (Jafari, Ghanbari, Ganje, & Dehnad, ), and turnip slices (Kaveh & Chayjan, ). But, there is no study about comparison between ANN model and mathematical models for PVOD treated and non‐pretreated figs during drying.…”

Section: Introductionmentioning

confidence: 99%

Comparison between Artificial Neural Network model and mathematical models for drying kinetics of osmotically dehydrated and fresh figs under open sun drying

Şahin

Öztürk

2018

In this study, drying kinetics of figs (Ficus carica L) under open sun drying (OSD) was compared with using Artificial Neural Network (ANN) model and mathematical models. Pulsed vacuum osmotic dehydration (PVOD) was applied as a pretreatment. In experiments, whole figs (Sarılop variety) were immersed in sucrose solution at 50°Brix and 50 °C with a ratio of solution/food was 4:1 for 180 min. Vacuum impregnation was applied at 253 mbar for 15 min. Shrinkage effect was considered, also. Results showed that PVOD reduced drying time of figs. The mean of Deff values of PVOD treated and fresh figs were obtained as 2.55 × 10−10 m2/s and 2.36 × 10−10 m2/s, respectively. It was found that present model was the best drying model with the highest correlation coefficient (R2) values among the mathematical models whereas ANN model had a higher correlation coefficient (R2 = 0.9999) values than the mathematical models for both PVOD treated and fresh figs. Practical applications Dried figs have an economic income for Turkey and Turkey is the biggest dried fig producer and exporter in the World. Drying of figs is very important phenomena because figs have a short harvesting time. Figs are widely dried under open sun because this method has low operating cost. OD method is used before drying step and provides reducing drying time. This study presents experimental results of osmotically dehydrated and fresh figs under open sun drying. Drying kinetics of figs were modeled with empirical and semiempirical models under shrinkage consideration. Shrinkage of fresh and osmotically dehydrated figs during the drying were mathematically modeled thus these correlations can be helpful for researchers. Also, a new mathematical model was established and this model had more accuracy than other mathematical models. The ANN model was used for the prediction of drying curves of figs in different operating conditions.