Modeling of the drying process of apple slices: Application with a solar dryer and the thermal energy storage system

Atalay, Halil; Çoban, Mustafa Turhan; Kıncay, Olcay

doi:10.1016/j.energy.2017.06.030

Cited by 66 publications

(19 citation statements)

References 26 publications

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“…From Figure 8, results show that the effect of air velocity is more significant at higher temperatures. Similar correlations were found by other authors [16,37,51]. An increase in the slice thickness led to an increased effective diffusion coefficient.…”

Section: Effective Diffusion Coefficient D Effsupporting

confidence: 90%

Experimental Study and Mathematical Modeling of Convective Thin-Layer Drying of Apple Slices

2020

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This work represents an experimental study and mathematical modeling of convective apple slice drying. The influence of multiple process parameters such as temperature, air humidity, air velocity and slice thickness on process kinetics, product water activity and parameters of empirical models has been investigated. Drying characteristics of apple slices were monitored at temperatures of 40, 45 and 50 °C, air velocities of 0.6, 0.85 and 1.1 m/s., slice thicknesses of 4, 6, 8, 10 and 12 mm, and in relative air humidity ranges of 25–28, 35–38 and 40–45%. During the process, samples were dried from an initial moisture content of 86.7% to that of 20% (w.b), corresponding to product water activity of 0.45 ± 0.05. By increasing the temperature from 40 to 50 °C, the time for reaching the required product water activity decreased by about 300 min. Sample thickness is the most significant parameter; by increasing the slice thickness from 4 to 12 mm, the time required to achieve the required water activity increased by more than 500 min. For all experimental runs, parameters of five different thin-layer empirical models were estimated. A thin-layer model sensible to process conditions such as temperature, air velocity, layer thickness and air relative humidity was developed and statistically analyzed.

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Section: Effective Diffusion Coefficient D Effsupporting

confidence: 90%

Experimental Study and Mathematical Modeling of Convective Thin-Layer Drying of Apple Slices

2020

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“…In Equation 5,ṁ is the mass flow (kg/s) of the air passing through the collector, C p is the specific heat of the air (J/kgK), T i and T o are the collector air inlet and outlet temperatures ( • C) respectively. The mass flow rate of the air passing through the collector was calculated by Equation (6).…”

Section: Theoretical Informationmentioning

confidence: 99%

Investigation of Mass Transfer with Different Models in a Solar Energy Food-Drying System

Demirpolat

2019

Energies

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In drying systems, the examination of the drying rate values of the food product in advance gives important information about the raw material to be dried. In this study, thin-layer drying behavior of apple slices in a convective solar dryer was investigated. The experiments were carried out at a drying air temperature of 46–63 °C and a drying air speed of 0.7–1.8 m/s. In order to determine the drying kinetics, the mass change of apple slices was recorded under all drying air conditions. The effects of drying air temperature and speed, drying speed of apple slices, dimensionless moisture content, were investigated. In a solar drying system, thermal efficiency, solar radiation and air velocity values were measured. The drying kinetics of 15-mm thick apple slices were examined for three days in the solar drying system. Using the decision tree algorithm, which is a machine learning algorithm, a predictive model was created for moisture rate in drying experiments and four linear equations were obtained. According to obtained equations, the collector in the drying system depends on the inlet–outlet temperature values, the drying room inlet–outlet temperature values, the drying room humidity values and air velocity values. Moisture rate data were applied to twelve different models and their performance was determined by root mean square error (RMSE) analysis. The mathematical model with the least error rate was (RMSE: 0.09) Midilli model. A comparison was made between these drying models in the literature and the model generated by the decision tree algorithm. According to the results of RMSE error analysis, it was shown that the model created with the decision tree algorithm predicted the moisture rate values with less error values RMSE: 0.03) than the Midilli model.

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“…Sun or solar drying and conventional hot‐air drying can be applied for quince samples. Sun drying of fruits has disadvantages such as longtime of drying, pollutions, and undesirable product (Atalay, Çoban, & Kincay, ). These disadvantages are sufficient to substitute industrial and technological trends for drying to guarantee demands and preserve the quality.…”

Section: Introductionmentioning

confidence: 99%

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

Taghinezhad

Kaveh

Jahanbakhshi

et al. 2020

J Food Process Engineering

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In the study, drying process of quince fruit was accomplished in a microwave‐convective dryer (MCD). The experiments were carried out at microwave power levels of 100, 200, and 300 W, air temperatures of 40, 55, and 70°C, and air velocities of 0.5, 1, and 1.5 m/s. Nevertheless, three artificial intelligence techniques consisted of artificial neural networks (ANNs), particle swarm optimizer (PSO), and grey wolf optimizer (GWO) were evaluated to predict the parameters of Deff, SEC, ΔE, and Sb. In the evaluation the data by ANNs, input parameters of networks consisted the values of air temperature, microwave power, and air velocity. According to the results, the maximum values of effective moisture diffusivity ( Deff) and specific energy consumption (SEC) were 1.71 × 10−9 m2/s and 126.07 kWh/kg, respectively. In addition, minimum values of total change in color (ΔE) and shrinkage ( Sb) of quince achieved 10.85 and 33.85%, respectively. For predicting all parameters, three models used in the study represented good predictive capability with R2 > 0.97. The obtained results showed that the GWO model had better predictive performance than the ANN and PSO models. Practical Application Drying food and agricultural products by application of microwave‐hot air blend dryers can be a good alternative to hot air and microwave dryers. Microwave energy infiltrates the product and facilitates heat release from the product and thus reduces drying time compared to single dryers. The main aim of applying such different models, mathematical simulation or modeling in the drying technology of agricultural products is to transform physical qualities and their interactions into numerical quantities and mathematical relationships.

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Modeling of the drying process of apple slices: Application with a solar dryer and the thermal energy storage system

Cited by 66 publications

References 26 publications

Experimental Study and Mathematical Modeling of Convective Thin-Layer Drying of Apple Slices

Experimental Study and Mathematical Modeling of Convective Thin-Layer Drying of Apple Slices

Investigation of Mass Transfer with Different Models in a Solar Energy Food-Drying System

Use of artificial intelligence for the estimation of effective moisture diffusivity, specific energy consumption, color and shrinkage in quince drying

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