scholarly journals Artificial Neural Network Modeling of Drying Kinetics and Color Changes of Ginkgo Biloba Seeds during Microwave Drying Process

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Jun-Wen Bai ◽  
Hong-Wei Xiao ◽  
Hai-Le Ma ◽  
Cun-Shan Zhou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ginkgo Biloba ◽  
Drying Kinetics ◽  
Microwave Drying ◽  
Drying Process ◽  
Drying Time ◽  
Color Changes ◽  
Rate Period ◽  
Artificial Neural

Ginkgo biloba seeds were dried in microwave drier under different microwave powers (200, 280, 460, and 640 W) to determinate the drying kinetics and color changes during drying process. Drying curves of all samples showed a long constant rate period and falling rate period along with a short heating period. The effective moisture diffusivities were found to be 3.318 × 10−9 to 1.073 × 10−8 m2/s within the range of microwave output levels and activation energy was 4.111 W/g. The L⁎ and b⁎ values of seeds decreased with drying time. However, a⁎ value decreased firstly and then increased with the increase of drying time. Artificial neural network (ANN) modeling was employed to predict the moisture ratio and color parameters (L⁎, a⁎, and b⁎). The ANN model was trained for finite iteration calculation with Levenberg-Marquardt algorithm as the training function and tansig-purelin as the network transfer function. Results showed that the ANN methodology could precisely predict experimental data with high correlation coefficient (0.9056–0.9834) and low mean square error (0.0014–2.2044). In addition, the established ANN models can be used for online prediction of moisture content and color changes of ginkgo biloba seeds during microwave drying process.

scholarly journals Modeling and Optimization for Konjac Vacuum Drying Based on Response Surface Methodology (RSM) and Artificial Neural Network (ANN)

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1430
Author(s):  
Zhiheng Zeng ◽  
Ming Chen ◽  
Xiaoming Wang ◽  
Weibin Wu ◽  
Zefeng Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Vacuum Drying ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

To reveal quality change rules and establish the predicting model of konjac vacuum drying, a response surface methodology was adopted to optimize and analyze the vacuum drying process, while an artificial neural network (ANN) was applied to model the drying process and compare with the response surface methodology (RSM) model. The different material thickness (MT) of konjac samples (2, 4 and 6mm) were dehydrated at temperatures (DT) of 50, 60 and 70 °C with vacuum degrees (DV) of 0.04, 0.05 and 0.06 MPa, followed by Box–Behnken design. Dehydrated samples were analyzed for drying time (t), konjac glucomannan content (KGM) and whiteness index (WI). The results showed that the DT and MT should be, respectively, under 60 °C and 4 mm for quality and efficiency purposes. Optimal conditions were found to be: DT of 60.34 °C; DV of 0.06 MPa and MT of 2 mm, and the corresponding responses t, KGM and WI were 5 h, 61.96% and 82, respectively. Moreover, a 3-10-3 ANN model was established to compare with three second order polynomial models established by the RSM, the result showed that the RSM models were superior in predicting capacity (R2 > 0.928; MSE < 1.46; MAE < 1.04; RMSE < 1.21) than the ANN model. The main results may provide some theoretical and technical basis for the konjac vacuum drying and the designing of related equipment.

Artificial Neural Network Approach for Impingement Drying of Germinated Brown Rice Soaking with Turmeric Solution

2013 ◽  
Vol 372 ◽  
pp. 463-466
Author(s):  
Kiattisak Suntaro ◽  
Khwanruedi Sangchum ◽  
Supawan Tirawanichakul ◽  
Yutthana Tirawanichakul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Brown Rice ◽  
Measured Data ◽  
Drying Kinetics ◽  
Drying Time ◽  
Rice Samples ◽  
Artificial Neural ◽  
Drying Conditions

The objectives of this research are to determine the evolution of moisture transfer for germinated Thai jasmine Khao Dawk Mali 105 (KDML105) brown rice variety using impingement drying by eight commonly empirical drying modeling and artificial neural network (ANN) method. The experiments were carried out with drying temperatures of 80-100°C, initial moisture content of KDML105 rice samples soaking with turmeric solution was of 54-55% dry-basis and the desired final moisture content for each drying conditions was fixed at 14-16% dry-basis. The air flow rate was fixed at 7.0 m/s. The measured data in each drying conditions were simulated for getting drying equation by non-linear regression analysis. The results showed that the rice soaking with herb turmeric solution had no effect to drying kinetics and the simulated data using empirical drying equation of Henderson model had the best fitting to all measured data (R2of 0.9978-0.9995 and RMSE of 0.0001441-0.000414). For applying ANN modeling approach, the drying temperature and drying time were considered as the input variables for the topology of neural network while the moisture ratio was the output layer. The simulation results concluded that the simulated values of the ANN model, which was not concerned with any complicated physical properties of grain rice kernels, could be used for prediction drying kinetics and was relatively high accuracy compared to those predicted results of empirical models. So the ANN method without any complicated properties related of rice samples can approach for good prediction their drying kinetics as well as the complicated drying simulations method.

Hybrid response surface methodology–artificial neural network optimization of drying process of banana slices in a forced convective dryer

2017 ◽  
Vol 24 (4) ◽  
pp. 277-291 ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Fatemeh Karimi ◽  
Mahmoud Karimi ◽  
Valiullah Lotfi ◽  
Golmohammad Khoobbakht
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Drying Rate ◽  
Drying Process ◽  
Air Velocity ◽  
Drying Time ◽  
Artificial Neural

The aim of the study is to fit models for predicting surfaces using the response surface methodology and the artificial neural network to optimize for obtaining the maximum acceptability using desirability functions methodology in a hot air drying process of banana slices. The drying air temperature, air velocity, and drying time were chosen as independent factors and moisture content, drying rate, energy efficiency, and exergy efficiency were dependent variables or responses in the mentioned drying process. A rotatable central composite design as an adequate method was used to develop models for the responses in the response surface methodology. Moreover, isoresponse contour plots were useful to predict the results by performing only a limited set of experiments. The optimum operating conditions obtained from the artificial neural network models were moisture content 0.14 g/g, drying rate 1.03 g water/g h, energy efficiency 0.61, and exergy efficiency 0.91, when the air temperature, air velocity, and drying time values were equal to −0.42 (74.2 ℃), 1.00 (1.50 m/s), and −0.17 (2.50 h) in the coded units, respectively.

scholarly journals Drying Klunzinger's mullet fish Planiliza klunzingeri using Halogen Dryer and modeling the moisture content with artificial neural network

2020 ◽  
Vol 33 (1) ◽  
pp. 231-261
Author(s):  
Hassan H. Al-Rubaiy ◽  
, Sabah M. Al-Shatty ◽  
Asaad R. Al-Hilphy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Chemical Composition ◽  
Moisture Content ◽  
Ph Value ◽  
Ann Model ◽  
Drying Process ◽  
Drying Time ◽  
Dried Fish ◽  
Artificial Neural

Salted and unsalted Klunzinger's mullet Planiliza klunzingeri were dried using infrared halogen dryer with different temperatures (60, 65, 70, 75 and 80)°C and  different storage periods (0, 7, 14, 21, 28 and 35) days and studying their qualitative characteristics. The results showed that the moisture content decreased as drying time increased. The drying efficiency of the halogen dryer was 70.36 % at 60 °C and decreased as the drying temperature increased. Chemical composition of dried fish (salted and unsalted) showed that the moisture percentage was decreased, but the percentage of protein, fat and ash was increased after drying process. The percentage of moisture increased during the storage periods (0, 7, 14, 21, 28 and 35) days, unlike the other chemical composition percentages were decreased with increasing storage periods. The results showed that the rehydration was decreased with the increase of drying temperatures for salted and unsalted dried fish. Moreover, the results showed that there was an increase in TBA after the drying process and during the storage periods. In addition, the results revealed that the microbial content of dried salted and unsalted fish was decreased. The results illustrated that the first order model can be used to predict pH value during storage periods. Artificial neural network   (ANN) model had a good result of predicted moisture content.

scholarly journals An artificial neural network model for predicting characteristic input parameters for physics based modelling of drying process

2019 ◽  
Author(s):  
Ankita Sinha ◽  
Atul Bhargav
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Estimation Methods ◽  
Drying Process ◽  
Many Instruments ◽  
Input Parameters ◽  
Artificial Neural

Drying is crucial in the quality preservation of food materials. Physics-based models are effective tools to optimally control the drying process. However, these models require accurate thermo-physical properties; unavailability or uncertainty in the values of these properties increases the possibility of error. Property estimation methods are not standardized, and usually involve the use of many instruments and are time-consuming. In this work, we have developed an experimentally validated deep learning-based artificial neural network model that estimates sensitive input parameters of food materials using temperature and moisture data from a set of simple experiments. This model predicts input parameters with error less than 1%. Further, using input parameters, physics-based model predicts temperature and moisture to within 5% accuracy of experiments. The proposed work when interfaced with food machinery could play a significant role in process optimization in food processing industries.

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