Fouling analysis of a shell and tube heat exchanger using local linear wavelet neural network

2014 ◽  
Vol 77 ◽  
pp. 946-955 ◽  
Author(s):  
Dillip Kumar Mohanty ◽  
Pravin M. Singru
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Wavelet Neural Network ◽  
Tube Heat Exchanger ◽  
Local Linear ◽  
Shell And Tube

Comparison of Static and Dynamic Neural Network Models in Predicting Outlet Temperature of Shell and Tube Heat Exchanger

2019 ◽  
Vol 11 (5) ◽  
Author(s):  
J. Manikandan ◽  
Yerram Naidu ◽  
B. Janardhan ◽  
M. Raj Kishore ◽  
K. Prabhakar
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Dynamic Network ◽  
Network Models ◽  
Outlet Temperature ◽  
Validation Data ◽  
Dynamic Neural Network ◽  
Tube Heat Exchanger ◽  
Dynamic Network Models ◽  
Shell And Tube

This paper presents the comparison of static and dynamic neural network (NN), model to predict the exit temperature of the heat exchangers. Feed forward NN was used as a static network while Time delay NN was used for a dynamic network. Experimental data was collected from a shell and tube heat exchanger to provide sufficient data processing, namely training, test and validation data to develop the models. The static and dynamic network models of the heat exchanger have been developed using Matlab. The performances of the models were evaluated by their statistical validity using the correlation co-efficient and the mean squared error. For time series predictions, the dynamic NN has shown better results than the static NN.

scholarly journals Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

2021 ◽  
Vol 13 (16) ◽  
pp. 8824
Author(s):  
Amir Zolghadri ◽  
Heydar Maddah ◽  
 Mohammad Hossein Ahmadi ◽  
Mohsen Sharifpur
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nusselt Number ◽  
Energy Consumption ◽  
Heat Exchanger ◽  
Aluminum Oxide ◽  
Mean Square ◽  
Tube Heat Exchanger ◽  
Shell And Tube ◽  
Artificial Neural

This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of eMAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2.

A Local Linear Wavelet Neural Network Based on a Bayesian Design Method

2009 ◽  
Vol 129 (7) ◽  
pp. 1356-1362
Author(s):  
Kunikazu Kobayashi ◽  
Masanao Obayashi ◽  
Takashi Kuremoto
Keyword(s):  
Neural Network ◽  
Design Method ◽  
Wavelet Neural Network ◽  
Bayesian Design ◽  
Local Linear

A Shell-and-Tube Heat Exchanger Optimization by Differential Evolution

2016 ◽  
Author(s):  
Leonardo Cavalheiro Martinez ◽  
Leonardo Cavalheiro Martinez ◽  
Viviana Mariani ◽  
Marcos Batistella Lopes
Keyword(s):  
Heat Exchanger ◽  
Differential Evolution ◽  
Tube Heat Exchanger ◽  
Shell And Tube
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