Prediction of Mass-transfer Kinetics and Product Quality Changes during a Dehydration-Impregnation-Soaking Process using Artificial Neural Networks. Application to Pork Curing

LWT ◽  
2002 ◽  
Vol 35 (8) ◽  
pp. 748-756 ◽  
Author(s):  
I. Poligné ◽  
B. Broyart ◽  
G. Trystram ◽  
A. Collignan
Keyword(s):  
Neural Networks ◽  
Mass Transfer ◽  
Artificial Neural Networks ◽  
Product Quality ◽  
Quality Changes ◽  
Mass Transfer Kinetics ◽  
Artificial Neural

scholarly journals Performance Assessment of an NH3/LiNO3 Bubble Plate Absorber Applying a Semi-Empirical Model and Artificial Neural Networks

Energies ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 4313
Author(s):  
Carlos Amaris ◽  
Maria E. Alvarez ◽  
Manel Vallès ◽  
Mahmoud Bourouis
Keyword(s):  
Neural Networks ◽  
Mass Transfer ◽  
Artificial Neural Networks ◽  
Heat And Mass Transfer ◽  
Empirical Model ◽  
Mass Flux ◽  
Ann Model ◽  
Artificial Neural ◽  
Semi Empirical ◽  
Plate Absorber

In this study, ammonia vapor absorption with NH3/LiNO3 was assessed using correlations derived from a semi-empirical model, and artificial neural networks (ANNs). The absorption process was studied in an H-type corrugated plate absorber working in bubble mode under the conditions of an absorption chiller machine driven by low-temperature heat sources. The semi-empirical model is based on discretized heat and mass balances, and heat and mass transfer correlations, proposed and developed from experimental data. The ANN model consists of five trained artificial neurons, six inputs (inlet flows and temperatures, solution pressure, and concentration), and three outputs (absorption mass flux, and solution heat and mass transfer coefficients). The semi-empirical model allows estimation of temperatures and concentration along the absorber, in addition to overall heat and mass transfer. Furthermore, the ANN design estimates overall heat and mass transfer without the need for internal details of the absorption phenomenon and thermophysical properties. Results show that the semi-empirical model predicts the absorption mass flux and heat flow with maximum errors of 15.8% and 12.5%, respectively. Maximum errors of the ANN model are 10.8% and 11.3% for the mass flux and thermal load, respectively.

scholarly journals Error Reduction In The Vehicle Wheel Rim Packaging With Image Processing And ANN

2019 ◽  
pp. 80-84
Author(s):  
Gokhan Gokay ◽  
Tulay Yildirim
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Networks ◽  
Product Quality ◽  
Initial Phase ◽  
Error Reduction ◽  
Wheel Rim ◽  
Artificial Neural ◽  
Product Control

The product quality is provided by the product control companies during the period from the initial phase. Of manufacturing to packaging in many companies. In this study, it is aimed to carry out product control with an image processing based algorithm to prevent model mixes that are loaded on pallets to be packed and cargoed while being controlled by the operators according to the rim models. In the method, a camera located at a height of 5 meters from the ground is used, and the features of the rims in the ground are extracted trained by using artificial neural networks. Attempted to prevent mixing in the backplane.

Convective Drying of Apples: Kinetic Study, Evaluation of Mass Transfer Properties and Data Analysis using Artificial Neural Networks

2014 ◽  
Vol 10 (2) ◽  
pp. 281-299 ◽  
Author(s):  
Raquel P. F. Guiné ◽  
Ana C. Cruz ◽  
M. Mendes
Keyword(s):  
Neural Networks ◽  
Mass Transfer ◽  
Artificial Neural Networks ◽  
Convective Drying ◽  
Network Modelling ◽  
Feed Forward ◽  
Study Evaluation ◽  
Feed Forward Neural Networks ◽  
Artificial Neural ◽  
Transfer Properties

Abstract In the present work, the effect of drying was evaluated on some chemical and physical properties of apples, and the functions were modelled using feed-forward artificial neural networks. The drying kinetics and the mass transfer properties were also studied. The results indicated that acidity and sugars were significantly reduced by drying. Regarding colour lightness decreases, whereas redness and yellowness increased. As for texture, the dried samples were softer and less cohesive as compared to the fresh ones. Mass diffusivity increased with temperature, from 4.4×10−10 m2/s at 30°C to 1.4×10−9 m2/s at 60°C, and so did the mass transfer coefficient, increasing from 3.7×10−10 m/s at 30°C to 7.4×10−9 m/s at 60°C. As to the activation energy, it was found to be 34 kJ/mol. Neural network modelling showed that all properties can be correctly predicted by feed-forward neural networks. The analysis of the networks’ behaviours input layer weight values also shows which properties are more affected by dehydration or more dependent on variety.

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