scholarly journals Application of Artificial Neural Network for Prediction of Key Indexes of Corn Industrial Drying by Considering the Ambient Conditions

2020 ◽  
Vol 10 (16) ◽  
pp. 5659 ◽  
Author(s):  
Bin Li ◽  
Chengjie Li ◽  
Junying Huang ◽  
Changyou Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Exchanger ◽  
Moisture Content ◽  
Radiant Energy ◽  
Prediction Performance ◽  
Drying Process ◽  
Ambient Conditions ◽  
Drying System ◽  
Artificial Neural

Uncontrollable ambient conditions are the main factors limiting the self-adaption control of an industrial drying system. To achieve the goal of accurate control of the drying process, the influence of the ambient conditions on the drying behavior should be taken into consideration when modeling the drying process. Present work introduced an industrial drying system with a loading capacity of 50 t, two artificial neural network prediction models with (IANN) and without (OANN) considering the ambient conditions were established using artificial neural network modeling approach. The ambient conditions on the moisture content (MC), exergy efficiency of the heat exchanger (ηex,h) and specific recovered radiant energy (Er) of the drying process were also investigated. The results showed that the ηex,h and Er increase with the increase of ambient temperature while the drying time decrease with the increase of the ambient temperature. The IANN model has a better prediction performance that that of OANN model. An optimal architecture of 9-2-12-3 artificial neuron network model was developed and the best prediction performance of the artificial neural network (ANN) model were found at a training epoch number of 30, and a momentum coefficient of 0.4, where the coefficient of determination of moisture content, exergy efficiency of heat exchanger, and the specific recovered radiant energy, respectively are 0.998, 0.992, and 0.980, indicating that the model has an excellent prediction performance and can be used in engineering practice.

Wood-water sorption isotherm prediction with artificial neural networks: A preliminary study

Holzforschung ◽  
2005 ◽  
Vol 59 (3) ◽  
pp. 336-341 ◽  
Author(s):  
Stavros Avramidis ◽  
Lazaros Iliadis
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Moisture Content ◽  
Sorption Isotherm ◽  
Ambient Conditions ◽  
Water Sorption Isotherm ◽  
Different Temperatures ◽  
Preliminary Study ◽  
Artificial Neural

Abstract This is a preliminary study that proposes an original prototype artificial neural network to be used in addition to the two classic sorption isotherm modeling methods, Hailwood-Horrobin (HH) and Guggenheim-Anderson-deBoer (GAB), in predicting the equilibrium moisture content in wood at three different temperatures (30, 45 and 60°C) for softwood (lodgepole pine) sapwood and heartwood specimens. Contrary to the HH and GAB equations, which use physical data for modeling, the predictive power of the artificial neural network is based on both physical and chemical data for the specific wood types. The results prove the potential efficient use of neural networks in predicting moisture content based not only on the ambient conditions, but also on taking into consideration the chemical composition of wood.

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.

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 Prediction and Analysis of Tensile Properties of Austenitic Stainless Steel Using Artificial Neural Network

Metals ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 234 ◽  
Author(s):  
Yuxuan Wang ◽  
Xuebang Wu ◽  
Xiangyan Li ◽  
Zhuoming Xie ◽  
Rui Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Stainless Steel ◽  
Austenitic Stainless Steel ◽  
Tensile Properties ◽  
Prediction Performance ◽  
Good Prediction ◽  
Relative Significance ◽  
Value Analysis ◽  
Artificial Neural

Predicting mechanical properties of metals from big data is of great importance to materials engineering. The present work aims at applying artificial neural network (ANN) models to predict the tensile properties including yield strength (YS) and ultimate tensile strength (UTS) on austenitic stainless steel as a function of chemical composition, heat treatment and test temperature. The developed models have good prediction performance for YS and UTS, with R values over 0.93. The models were also tested to verify the reliability and accuracy in the context of metallurgical principles and other data published in the literature. In addition, the mean impact value analysis was conducted to quantitatively examine the relative significance of each input variable for the improvement of prediction performance. The trained models can be used as a guideline for the preparation and development of new austenitic stainless steels with the required tensile properties.

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