Investigation of the drying kinetics of brewer's spent grain ( BSG ) using artificial neural networks and traditional empirical models

2021 ◽  
Author(s):  
Fabio Henrique Bastiani ◽  
Bruno Telli Ceccato ◽  
Douglas Junior Nicolin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Drying Kinetics ◽  
Brewer’S Spent Grain ◽  
Spent Grain ◽  
Artificial Neural ◽  
Kinetics Of

scholarly journals Prediction of paddy drying kinetics: A comparative study between mathematical and artificial neural network modelling

2017 ◽  
Vol 23 (2) ◽  
pp. 251-258 ◽  
Author(s):  
Mohsen Beigi ◽  
Mehdi Torki-Harchegani ◽  
Mahmood Mahmoodi-Eshkaftaki
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Comparative Study ◽  
Mean Square Error ◽  
Back Propagation ◽  
Drying Kinetics ◽  
Mean Square ◽  
Rough Rice ◽  
Artificial Neural ◽  
Drying Curves

The present study aimed at investigation of deep bed drying of rough rice kernels at various thin layers at different drying air temperatures and flow rates. A comparative study was performed between mathematical thin layer models and artificial neural networks to estimate the drying curves of rough rice. The suitability of nine mathematical models in simulating the drying kinetics was examined and the Midilli model was determined as the best approach for describing drying curves. Different feed forward-back propagation artificial neural networks were examined to predict the moisture content variations of the grains. The ANN with 4-18-18-1 topology, transfer function of hyperbolic tangent sigmoid and a Levenberg-Marquardt back propagation training algorithm provided the best results with the maximum correlation coefficient and the minimum mean square error values. Furthermore, it was revealed that ANN modeling had better performance in prediction of drying curves with lower root mean square error values.

scholarly journals Radyal Tabanlı Yapay Sinir Ağlarının Kurutma Kinetiğinin Modellenmesinde Kullanımı

2020 ◽  
Vol 8 (2) ◽  
pp. 511
Author(s):  
Adil Koray Yıldız ◽  
Muhammed Taşova ◽  
Hakan Polatcı
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Drying Kinetics ◽  
Hot Pepper ◽  
Neural Network Models ◽  
Drying Time ◽  
Drying Behavior ◽  
Artificial Neural ◽  
Hidden Layer

Drying method is preferred in agricultural products since it provides advantages in many processes such as increasing the strength of products, transporting and storing. It is necessary to estimate the drying behavior of the products in order to achieve the best drying without reducing the product quality. For this reason, many numerical drying models have been developed to estimate the drying kinetics of the products. Recently, artificial neural networks have been widely used for the development of these models. Artificial neural networks are mathematical models that work in a similar way to natural neuron cells. Radial based artificial neural networks are radial based activation functions in the transition to the hidden layer unlike other networks. In this study, modeling of drying kinetics with radial based networks was investigated. For the experiment, red hot pepper (Capsicum annuum L.) was dipped in boiled water and microwave pretreatments and, then dried in the oven at 65°C. The absorbable moisture values were calculated during the drying period. The radial based artificial neural network models were trained with the drying time values as input and the absorbable moisture values as output. The study was carried out with two data sets including all data and only the average. In trainings with all data, R value of the best model was calculated as 0.9566. R was calculated as 0.9998 with average data.

Application of Artificial Neural Networks in Assessing the Equilibrium Depth of Local Scour Around Bridge Piers

2015 ◽  
Author(s):  
Ehsan Sarshari ◽  
Philippe Mullhaupt
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Empirical Models ◽  
Local Scour ◽  
Bridge Piers ◽  
Local Conditions ◽  
The Neural Network ◽  
Artificial Neural

Scour can have the effect of subsidence of the piers in bridges, which can ultimately lead to the total collapse of these systems. Effective bridge design needs appropriate information on the equilibrium depth of local scour. The flow field around bridge piers is complex so that deriving a theoretical model for predicting the exact equilibrium depth of local scour seems to be near impossible. On the other hand, the assessment of empirical models highly depends on local conditions, which is usually too conservative. In the present study, artificial neural networks are used to estimate the equilibrium depth of the local scour around bridge piers. Assuming such equilibrium depth is a function of five variables, and using experimental data, a neural network model is trained to predict this equilibrium depth. Multilayer neural networks with backpropagation algorithm with different learning rules are investigated and implemented. Different methods of data normalization besides the effect of initial weightings and overtraining phenomenon are addressed. The results show well adoption of the neural network predictions against experimental data in comparison with the estimation of empirical models.

scholarly journals A Hybrid Path Loss Prediction Model based on Artificial Neural Networks using Empirical Models for LTE And LTE-A at 800 MHz and 2600 MHz

2017 ◽  
Vol 16 (3) ◽  
pp. 708-722 ◽  
Author(s):  
Bruno J. Cavalcanti ◽  
Gustavo A. Cavalcante ◽  
Laércio M. de Mendonça ◽  
Gabriel M. Cantanhede ◽  
Marcelo M.M. de Oliveira ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Path Loss ◽  
Empirical Models ◽  
Model Based ◽  
Loss Prediction ◽  
Path Loss Prediction ◽  
Artificial Neural
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