DYNAMIC MODELING OF PLATE HEAT EXCHANGER USING ARTIFICIAL NEURAL NETWORKS - Arisariyawong, Tanayos (Srinakharinwirot University Journal of Sciences and Technology, 2022)
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.