scholarly journals Identification of Heat Exchanger by Neural Network Autoregressive with Exogenous Input Model

2019 ◽  
Author(s):  
Tatang Mulyana
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Input Model

Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller

2015 ◽  
Vol 89 ◽  
pp. 1046-1053 ◽  
Author(s):  
Anna Vasičkaninová ◽  
Monika Bakošová
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Fuzzy Controller ◽  
Predictive Controller

Implementation of a neural network MPC for heat exchanger network temperature control

2020 ◽  
Vol 37 (4) ◽  
pp. 729-744 ◽  
Author(s):  
Carolina B. Carvalho ◽  
Esdras P. Carvalho ◽  
Mauro A. S. S. Ravagnani
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Temperature Control ◽  
Heat Exchanger Network

Predicting the efficiency of CuO/water nanofluid in heat pipe heat exchanger using neural network

2019 ◽  
Vol 104 ◽  
pp. 33-40 ◽  
Author(s):  
Heydar Maddah ◽  
Mahyar Ghazvini ◽  
Mohammad Hossein Ahmadi
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Heat Pipe ◽  
Pipe Heat

scholarly journals A Novel Analytical-ANN Hybrid Model for Borehole Heat Exchanger

Energies ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 6213
Author(s):  
Anjan Rao Puttige ◽  
Staffan Andersson ◽  
Ronny Östin ◽  
Thomas Olofsson
Keyword(s):  
Neural Network ◽  
Heat Exchanger ◽  
Analytical Model ◽  
Hybrid Model ◽  
Time Resolution ◽  
Network Models ◽  
Borehole Heat Exchanger ◽  
Neural Network Models ◽  
Ground Source

Optimizing the operation of ground source heat pumps requires simulation of both short-term and long-term response of the borehole heat exchanger. However, the current physical and neural network based models are not suited to handle the large range of time scales, especially for large borehole fields. In this study, we present a hybrid model for long-term simulation of BHE with high resolution in time. The model uses an analytical model with low time resolution to guide an artificial neural network model with high time resolution. We trained, tuned, and tested the hybrid model using measured data from a ground source heat pump in real operation. The performance of the hybrid model is compared with an analytical model, a calibrated analytical model, and three different types of neural network models. The hybrid model has a relative RMSE of 6% for the testing period compared to 22%, 14%, and 12% respectively for the analytical model, the calibrated analytical model, and the best of the three investigated neural network models. The hybrid model also has a reasonable computational time and was also found to be robust with regard to the model parameters used by the analytical model.

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