Abstract
This work used artificial neural network (ANN) to predict the heat transfer rates of shell-and-tube heat exchangers with segmental baffles or continuous helical baffles, based on limited experimental data. The Back Propagation (BP) algorithm was used in training the networks. Different network configurations were also studied. The deviation between the predicted results and experimental data was less than 2%. Comparison with correlation for prediction shows ANN superiority. It is recommended that ANN can be easily used to predict the performances of thermal systems in engineering applications, especially to model heat exchangers for heat transfer analysis.
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References
Thibault J, Grandjean B P. A neural network methodology for heat transfer data analysis Int J Heat and Mass Transfer, 1991,34:2063–2070
Jambunathan K, Hartle S L, Ashforth-Frost S, Fontama V. Evaluating heat transfer coefficients using neural networks. Int J Heat and Mass Transfer, 1996,39:2329–2332
Bittanti S, Piroddi L. Nonlinear Identification and Control of a Heat Exchanger — A Neural Network Approach. J Franklin Inst, 1997,334B: 135–153
Yang K T, Sen M. Artificial neural network-based dynamic modeling thermal systems and their control. Wang B X. Heat transfer science and technology. Beijing: Higher Education Press, 2000.
Sen M, Yang K T. Applications of artificial neural networks and genetic algorithms in thermal engineering. Kreith F. The CRC Handbook of Thermal Engineering. Boca Raton Fla: CRC Press, 2000.
Diaz G, Sen M, Yang K T, McClain R T. Simulation of heat exchanger performance by artificial neural networks. Int J HVAC&R Research, 1999,5:195–208
Diaz G Simulation and control of heat exchangers using artificial neural networks, PhD thesis, University of Notre Dame, 2000.
Diaz G, Sen M, Yang K T, McClain R T. Dynamic prediction and control of heat exchangers using artificial neural networks. Int J Heat and Mass Transfer, 2001,45:1671–1679
Diaz G, Sen M, Yang K T, McClain R T. Adaptive neuro-control of heat exchangers. ASME J Heat Transfer, 2001,123:417–612
Diaz G, Sen M, Yang K T, McClain R T. Stabilization of thermal neuro-controllers. Applied Artificial Intelligence, 2004,18:447–466
Pacheco-Vega A, Sen M, Yang KT, McClain R T. Neural network analysis of fin-tube refrigerating heat exchanger with limited experimental data. Int J Heat and Mass Transfer, 2001,44:763–770
Pacheco-Vega A, Diaz G, Sen M, Yang KT, McClain R T. Heat rate predictions in humid air-water heat exchangers using correlations and neural networks. ASME J Heat Transfer, 2001,123:348–354
Pacheco-Vega A. Simulation of compact heat exchangers using global regression and soft computing. PhD thesis, University of Notre Dame, 2002.
Pacheco-Vega A, Sen M, Yang KT. Simultaneous determination of in-and-over-tube heat transfer correlations in heat exchangers by global regression. Int J Heat and Mass Transfer, 2003,46:1029–1040
Islamoglu Y. A new approach for the prediction of the heat transfer rate of the wire-on-tube type heat exchanger-use of an artificial neural network model. Applied Thermal Engineering, 2003,23:243–249
Islamoglu Y, Kurt A. Heat transfers analysis using ANNs with experimental data with air flow in corrugated channels. Int J Heat and Mass Transfer, 2004,47:1361–1365
Ayoubi M. Dynamic multi-layer perception networks: application to the nonlinear identification and predictive control of a heat exchanger. World Scientific Series In Roboties and Intelligent System, 1997,17:205–230
Jalili-Kharaajoo M, Araabi B N. Neuro-predictive control of a heat exchanger: comparison with generalized predictive control. IEEE trans, 2003,675–678
Varshney K, Panigrahi P K. Artificial neural network control of a heat exchanger in a closed flow air circuit. Applied Soft Computing, 2005,5:441–465
Zhao X. Performance of a single-row heat exchanger at low in-tube flow rates. Master thesis, University of Notre Dame, 1995.
Peng B T. Experimental study of heat transfer and pressure drop for shell-and-tube heat exchangers with continuous helical baffles. Master thesis, Department of Energy and Power Engineering, Xi’an JiaoTong University, 2005 (in Chinese)
Haykin S. Neural Networks: a Comprehensive Foundation. Upper Saddle River, N.J: Prentice Hall, 1999
Hagan M T, Demuth H B, Beale M. Neural Network Design. Beijing: China Machine Press, 2002
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Wang, Q., Xie, G., Zeng, M. et al. Prediction of heat transfer rates for shell-and-tube heat exchangers by artificial neural networks approach. J. of Therm. Sci. 15, 257–262 (2006). https://doi.org/10.1007/s11630-006-0257-6
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DOI: https://doi.org/10.1007/s11630-006-0257-6