scholarly journals Artificial neural network techniques to predict the moisture ratio content during hot air drying and vacuum drying of Radix isatidis extract

2022 ◽  
Vol 7 (1) ◽  
pp. 5
Author(s):  
YouLu Li ◽  
Yao Liu ◽  
Jian Xu ◽  
YongPing Zhang ◽  
LuoNa Zhao ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Ratio ◽  
Vacuum Drying ◽  
Hot Air Drying ◽  
Air Drying ◽  
Hot Air ◽  
Radix Isatidis ◽  
Artificial Neural

Empirical Model and Artificial Neural Network Model Approach for Air Dried Sheets (ADS) Rubber

2012 ◽  
Vol 622-623 ◽  
pp. 69-74
Author(s):  
T. Ninchuewong ◽  
S. Tirawanichakul ◽  
Y. Tirawanichakul
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Hot Air Drying ◽  
Air Drying ◽  
Rubber Sheet ◽  
Hot Air ◽  
Drying Behavior

The objective of this research was to predict drying behavior of hot air drying using an empirical model (EM) and an artificial neural network model (ANN). Rubber sheet with initial moisture content ranging of 23-40% dry-basis was dried by temperature ranging of 40-70°C and air flow rate of 0.7 m/s. The desired final moisture content was set at 0.15% dry-basis. The results showed that drying rate of rubber sheet dried with hot air convection was faster than conventional natural aeration. The EM and ANN were simulated to describe the drying behavior of products. Furthermore, prediction results between EM and ANN were compared with the experimental data. In this research, it was obviously found that ANN can describe the drying behavior effectively. Additionally, it was also found that predicted results of Multilayer feed forward Levenberg-Maqurdt’s Back-propagation ANN were good agreement with the experimental results compared to those results of EM. It is the optimum architecture for prediction the evolution of moisture transfer for hot air drying.

scholarly journals Effects of Freeze Vacuum Drying Combined with Hot Air Drying on the Sensory Quality, Active Components, Moisture Mobility, Odors, and Microstructure of Kiwifruits

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Lihui Zhang ◽  
Yu Qiao ◽  
Chao Wang ◽  
Li Liao ◽  
Lu Liu ◽  
...  
Keyword(s):  
Sensory Quality ◽  
Antioxidant Properties ◽  
Bound Water ◽  
Principal Component ◽  
Vacuum Drying ◽  
Active Components ◽  
Hot Air Drying ◽  
Drying Time ◽  
Air Drying ◽  
Hot Air

In this study, freeze vacuum drying (FVD), hot air drying (AD), and FVD combined with AD (FVD-AD) were used to dry kiwifruits. Dried products were analyzed comprehensively on their sensory quality, active components, moisture mobility, odors, and microstructure. Results showed that the FVD-AD saved time by 38.22% compared with FVD while maintaining an acceptable product quality. The antioxidant properties of FVD-AD were lower than those of FVD but significantly higher than those of AD. Moreover, compared with FVD products, FVD-AD products were moderately hard (5252.71 ± 33.53 g) and improved in color, bound water, and microstructure. Additionally, FVD-AD consumed lesser drying time and energy than FD. According to cluster analysis, the odors of FVD-AD products were similar to those of the fresh ones. Principal component analysis of physicochemical and drying cost indicated that FVD-AD was a promising processing technique for functional kiwifruit snacks.

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.

An Analysis of Flavour Compounds of Bananas under Different Drying Methods on the Basis of GC-MS Computer Technology

2014 ◽  
Vol 644-650 ◽  
pp. 5336-5340 ◽  
Author(s):  
Bao Yu Li ◽  
Jun Yang ◽  
Kai Dan Yin ◽  
Fan Li Kong ◽  
Jin Feng Bi
Keyword(s):  
Computer Technology ◽  
Vacuum Drying ◽  
Drying Methods ◽  
Hot Air Drying ◽  
Air Drying ◽  
Flavour Compounds ◽  
Hot Air ◽  
Aroma Components

In order to study changes of aroma components of hot-air drying and vacuum drying banana slices, using SPME-GC/MS coupling on the aroma components were analysed bytechnology. Different drying methods kinds of aroma constituents and the content of banana samples have great differences. Hot air drying characteristic flavor substances are aldehydes, vacuum drying of flavor substances disappear is alcohols, acids and aldehydes.

Estimation of Moisture Ratio of a Mushroom Undergoing Microwave-vacuum Drying Using Artificial Neural Network and Regression Models

2007 ◽  
Vol 2 (3) ◽  
Author(s):  
Poonpat Poonnoy ◽  
Ampawan Tansakul ◽  
Manjeet Chinnan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Moisture Ratio ◽  
Vacuum Pressure ◽  
Ann Model ◽  
Drying Time ◽  
Feed Forward ◽  
Marquardt Algorithm ◽  
Ann Models ◽  
Artificial Neural

The drying rate of a mushroom undergoing microwave-vacuum (MV) drying (MVD) was controlled by moisture dissipation and was dependent on vacuum pressure levels. The main objective of this work was to develop artificial neural network (ANN) model to predict moisture ratio of MV-dried mushrooms. One-hidden-layer feed-forward ANN models were trained and validated with experimental data. The Levenberg-Marquardt algorithm was utilized in regulating the ANN model weights and biases. Inputs for ANN models were vacuum pressure and drying time. Output from ANN models was moisture ratio at a given drying time. Reduced chi-square (X 2) and root mean square error (RMSE), and residual sum of squares (RSS) of the results from ANN models were calculated and compared with those of a modified Page's model (an experimental-based mathematical model), which is commonly used in the literature. The X 2, RMSE, and RSS of the ANN model (2.272 x 10 -5, 4.023 x 10 -3, and 3.204 x 10 -3, respectively) were found to be lower than those of the modified Page's model (6.692 x 10 -4, 2.561 x 10 -2, and 12.98 x 10 -2, respectively). These results indicate that the feed-forward ANN model represented the drying characteristics of mushrooms better than the modified Page's model. Therefore, the ANN model could be considered as a better tool for estimation of the moisture content of mushrooms than by the modified Page's model.

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