Total views : 126

Artificial Neural Network Approach for Dynamic Modelling of Heat Exchanger for Data Prediction


  • Faculty of Computer Science and Engineering, Karpagam Institute of Technology, Karpagam University, Coimbatore – 641021, Tamil Nadu, India
  • Karpagam College of Engineering, Othakkal Mandapam – 641032, Tamil Nadu, India


Objective: Artificial Neural Networks became a powerful tool for dynamic modelling of non linear physical systems and for prediction of specific parameters of complex systems. This work aims to model a heat exchanger using different neural networks and to investigate their performance in predicting its outlet temperature. Methods: In this work four different neural networks namely Elman Recurrent Neural Networks (ERNN), Time Delay Neural Networks (TDNN), Cascade Feed Forward Neural Networks (CFFNN) and Feed Forward Neural Networks (FFNN) are modelled for the prediction of the outlet liquid temperature of a saturated steam heat exchanger from its liquid flow rate. A benchmark dataset consisting of 4000 tuples is used to train, validate and test the performance of each neural network model. Findings: All the four ANN models are trained, validated and tested which predicted the outlet temperature of the Heat exchanger with acceptable accuracy and the Elman Recurrent Neural Network is found to have the best accuracy by having the lowest Mean Square Error (MSE) and best Regression due to its feedback connections. Applications: The Artificial Neural Network Models simulated, especially the Elman Neural Networks are good at prediction of operational parameters of a physical system and hence can be used in prediction and forecasting of operational parameters of engineering, medical, financial and environmental systems.


Artificial Neural Networks, Elman Recurrent Neural Network, Heat Exchanger, Prediction.

Full Text:

 |  (PDF views: 120)


  • Diaz G, Sen M, Yang KT, McClain RL. Dynamic prediction and control of heat exchangers using artificial neural networks. International Journal of Heat and Mass Transfer. 2001 May; 44(9):1671–9.
  • Wang Q, Xie G, Zeng M, Luo L. Prediction of heat transfer rates for shell and tube heat exchangers by artificial neural networks approach. Journal of Thermal Science. 2006 Sep; 15(3):257–62.
  • Patra SR, Jehadeesan R, Rajeswari S, Satyamurthy SAV. Artificial neural network model for intermediate heat exchanger of nuclear reactor. International Journal of Computer Applications. 2010; 1(26):0975–8887.
  • Rotich NK, Backman J, Linnanen L, Daniil P. Wind resource assessment and forecast planning with neural networks. journal of sustainable development of energy. Water and Environment Systems. 2014; 2(2):174–90.
  • Russo SL, Taddia G, Gnavi L, Verda V. Neural network approach to prediction of temperatures around groundwater heat pump systems. Hydrogeology Journal. 2014 Feb; 22(1):205–16.
  • Gharehchopogh FS, Khaze SR, Maleki I. A new approach in bloggers classification with hybrid of k-nearest neighbor and artificial neural network algorithms. Indian Journal of Science and Technology. 2015 Feb; 8(3):237–46. DOI: 10.17485/ijst/2015/v8i3/59570.
  • Vasickaninova A, Bakosova M. Control of a heat exchanger using neural network predictive controller combined with auxiliary fuzzy controller. Applied Thermal Engineering. 2015 Oct 5; 89:1046–53.
  • Ahilan C, Kumanan S, Sivakumaran N. Online performance assessment of heat exchanger using artificial neural networks. International Journal of Energy and Environment. 2011; 2(5):829–44.
  • Xie GN, Wang QW, Zeng M, Luo LQ. Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural networks approach. Applied Thermal Engineering. 2007 Apr; 27(5–6):1096–104.
  • Nazir A. A comparative study of different artificial neural networks based intrusion detection systems. International Journal of Scientific and Research Publications. 2013 Jul; 3(7):2250–3153 11. Mummadisetty BC, Puri A, Sharifahmadian E, Latifi S. A hybrid method for compression of solar radiation data using neural networks. International Journal of Communications, Network and System Sciences. 2015; 8:217–28.
  • Akila S, Chandramathi S. A hybrid method for coronary heart disease risk prediction using decision tree and multi-layer perceptron. Indian Journal of Science and Technology, 2015 Dec; 8(34):0974–5645. DOI: 10.17485/ijst/2015/v8i34/85947.
  • Dhivya A, Sivanandan SN. Hybrid fuzzy Jordan network for robust and efficient intrusion detection system. Indian Journal of Science and Technology. 2015 Dec; 8(34):1–10. DOI: 10.17485/ijst/2015/v8i34/76697.
  • Sumathi A, Sundaram BV. An ANN approach in ensuring cia triangle using an energy based secured protocol E-AODV for enhancing the performance in MANETS. Indian Journal of Science and Technology. 2015 Dec; 8(34):1–10. DOI: 10.17485/ijst/2015/v8i34/IPL0821.
  • Bittanti S, Piroddi L. Nonlinear identification and control of a heat exchanger: A neural network approach. Journal of the Franklin Institute. 1997 Jan; 334(1):135–53.
  • Moor DBLR. DaISy: Database for the Identification of Systems, Department of Electrical Engineering, ESAT/STADIUS, KU Leuven, Belgium, [Used dataset: Liquid-saturated steam heat exchanger data set , Process Industry Systems section ,code number . [97-002] [Internet]. [cited 2016 Jan 09]. Available from: URL:
  • Badde DS, Gupta AK, Patki VK. Cascade and feed forward back propagation artificial neural network models for prediction of compressive strength of ready mix concrete. IOSR Journal of Mechanical and Civil Engineering; 2013. p. 01–06.
  • Neural Network Tool Box [Internet]. [cited 2015 Dec 01]. Available from:
  • Sundaram NM, Sivanandam SN. Intelligent classifier model employing hybrid ELMAN neural network architecture and biogeography based optimization for data classification. International Journal of Applied Engineering Research. 2015 Nov; 10(15):35027–38.
  • Sundaram NM. Optimization of training phase of Elman neural networks by suitable adjustments on the network parameters. Proceedings of the International Conference on Systems, Science, Control, Communication, Engineering and Technology, ICSSCCET; 2015.
  • Time delay neural network [Internet]. [cited 2015 Dec 01]. Available from:


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.