• Tanayos Arisariyawong Department of Mechanical Engineering, Faculty of Engineering, Srinakharinwirot University


Plate Heat Exchanger, Artificial Neural Networks, Dynamic Modeling


Plate heat exchanger is very popular in the industry because of good heat transfer efficiency compared to its size. Dynamic modeling of plate heat exchanger is importance in terms of design and predicting the process response. This research presents the use of artificial neural network to construct a dynamic model of plate heat exchangers showing the relationship between hot water flow rate and outlet cold water temperature over time and compared the results with dynamic model in terms of transfer function. From the experimental results showed that the mean squared error during the transient response of the neural network and the transfer function were 0.0003 and 0.0444, respectively. During steady state response the mean squared error of the neural network and the transfer function were 0.0002 and 0.0013, respectively. It was found that the dynamic model from artificial neural network gave better prediction results in both transient response and steady-state response.


Download data is not yet available.


Wang, S., Wang, S., and Dong, Y. (2004). Dynamic properties modeling and simulation of plate heat exchanger based on MATLAB. Computer Simulation, 21(10), 44-47.

Lamb, B. R. (1982). Plate heat exchangers, a low cost route to heat recovery. Journal of Heat Recovery Systems, 2(3), 247-255.

Xu, Z., Wen, X., Zheng, J., Guo, J., and Huang, X. (2011). Cooling water fouling resistance prediction of plate heat exchanger based on partial least squares regression. CIESC Journal, 62(6), 1531-1536.

Zhang, G., Li, G., Li, W., Huang, T., and Ren, Y. (2013). Experimental and theoretical investigations about particulate fouling in plate heat exchangers. Journal of Engineering Thermophysics, 34(9), 1715-1718.

Zheng, R., Jiang, Y., and Fang, X. (2010). Analysis of relative heat transfer coefficient of plate heat exchangers under variable flow conditions. Heating Ventilating & Air Conditioning, 40(10), 85-88.

Gut, J. A. W., Fernandes, R., Pinto, J. M., Tadini, C. C. (2004). Thermal model validation of plate heat exchangers with generalized configurations. Chemical Engineering Science, 59(21), 4591-4600.

Zhang, J., Wen, Y., Zhao, L., Li, D., and Wang, Y. (2015). Heat transfer and flow analysis and corrugation parameters optimization of the plate heat exchanger based on computational fluid dynamics numerical simulation. Journal of Mechanical Engineering, 51(12), 137-145.

Wu, J., Xia, M., Ye, L., and Han, D. (2012). A numerical study and thermal resistance analysis of heat transfer enhancement in plate heat exchangers. Journal of Engineering Thermophysics, 33(11), 1963-1966.

Xie, G. N., Wang, Q. W., Zeng, M., and Luo, L. Q. (2007). Heat transfer analysis for shell-and-tube heat exchangers with experimental data by artificial neural network approach. Applied Thermal Engineering, 27(5-6), 1096-1104.

Pacheco-Vega, A., Sen, M., and McClain, R. L. (2000). Analysis of fin tube evaporator performance with limited experimental data using artificial neural networks. In Proc. ASME Heat Transfer Division. pp. 95-101. HTD.

Tan C. K., Ward J., Wilcox S. J., and Payne R. (2009). Artificial neural network modeling of the thermal performance of a compact heat exchanger. Applied Thermal Engineering, 29(17), 3609-3617.

Yang, C., Zhang, L., and Zhou, J. (2010). A distributed parameter model and its application in optimizing the plate-fin heat exchanger based on the minimum entropy generation. International Journal of Thermal Sciences, 49(8), 1427-1436.

Burns, A. J. (1981). Dynamic system, measurement and control. Bioresource Technology. ASME.

Ghanim, M. (1982). Dynamics of plate heat exchanger [Unpublished master’s thesis]. University of Baghdad. Iraq.

Khan, A. R., Baker, N. S., and Wardle, A. P. (1988). The dynamic characteristics of a counter-current plate heat exchanger. International Journal of Heat and Mass Transfer, 31(6), 1269-1278.

Ramachandran, R., Lakshminarayanan, S., and Rangaiah, G. P. (2005). Process identification using open-loop and closed-loop step responses. Journal of The Institution of Engineers, 6(45), 1-13.

Haykin, S. (1994). Neural networks, A Comprehensive Foundation. New Jersey. Prentice Hall.




How to Cite

Arisariyawong, T. . (2022). DYNAMIC MODELING OF PLATE HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORKS. Srinakharinwirot University Journal of Sciences and Technology, 14(28, July-December), 65–78. Retrieved from https://ph02.tci-thaijo.org/index.php/swujournal/article/view/248079