The use of artificial neural network to predict exergetic performance of spray drying process: A preliminary study

2012 ◽  
Vol 88 ◽  
pp. 32-43 ◽  
Author(s):  
Mortaza Aghbashlo ◽  
Hossien Mobli ◽  
Shahin Rafiee ◽  
Ashkan Madadlou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Spray Drying ◽  
Drying Process ◽  
Preliminary Study ◽  
Artificial Neural

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.

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.

scholarly journals CLASSIFICATION OF THE ENERGY AND EXERGY OF MICROWAVE DRYERS IN DRYING KIWI USING ARTIFICIAL NEURAL NETWORKS

2019 ◽  
pp. 29-44
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Artificial Neural Network ◽  
Energy Loss ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Drying Process ◽  
Percent Correct ◽  
Energy And Exergy ◽  
Artificial Neural

ABSTRACT This investigation uses the artificial neural network model to classify the energy and exergy of the kiwi drying process in a microwave dryer. In this experiment, classification was carried out separately for various pretreatments and microwave powers using three pretreatments (oven, ohmic, and control treatments) and microwave power values (360, 600, and 900W), and the artificial neural network model. Classification was done using 5 different input data groups. The first group included the overall data (energy efficiency, special energy loss, exergy efficiency, and exergy loss), while the second to fifth groups included the data on the exergy efficiency, special energy loss, energy efficiency and special exergy loss in the order mentioned, which served as the classification inputs. Considering the results, the best R and Percent Correct values for the oven (Percent Correct=90 – R=0.709) and ohmic (Percent Correct=83.33– R=0.846) pretreatments were obtained. The values of this parameters were also calculated for the control (Percent Correct=71.43 – R=0.843), the 360W power (Percent Correct=92.86 – R=0.9975), the 600W power (Percent Correct=100 – R=0.9124), and the 900W power (Percent Correct=100 – R=0.9685). The overall data was used in the classification phase. In addition, the maximum correctly detected data for the oven, ohmic, and pretreatment was 18 (20 items), 15 (18 items), and 5 (7 items), respectively. The maximum correctly detected data for the 360W power, 600W power, and 900W power levels was 13 (14 items), 15 (15 items), and 16 (16 items), respectively. In sum, the neural network using the overall data input displayed acceptable efficiency in classifying the energy and exergy data of the kiwi drying process in microwave dryers

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