Abstract
The problem of heat exchange between two or more fluids at different temperatures is one of the most important and most common problems of engineering applications. In order to solve this problem efficiently, the transfer of energy between two liquids at different temperatures is carried out by heat exchangers. Heat exchangers increase the energy efficiency as they can transfer the energy contained in the system to another part of the process instead of just pumping and wasting. A plate heat exchanger, a variant of heat exchanger, use a series of thin plates to transfer heat between two liquids. Thermal modelling of the heat exchanger is important due to determination of the outlet temperature of fluids depending on the system parameters. In this paper, an artificial neural network (ANN) model is used to simulate the thermal performance of a chevron type plate heat exchanger using water as working fluid. The ANN algorithms have a widely usage in thermal analysis studies of heat exchangers such as modelling of heat exchangers, estimation of heat exchanger parameters, estimation of phase change characteristics in heat exchangers and control of heat exchangers. The outer temperatures of the water are estimated depending on the cold water mass flow rate, inlet hot water temperature and inlet cold water temperature by using limited experimental data. Then the experimental results and the estimated results are compared for testing the accuracy and reliability of the developed algorithm. The results show that the experimental and estimated results have a good agreement. The developed network structure estimates the outlet temperatures with 2.58 % and 1.80 % for hot and cold water, respectively. In addition, the predicted performance of the network developed by applying untested input parameters was examined. Estimation accuracy was compared with theoretically calculated output temperatures by thermal analysis using the same inputs. According to the obtained results, it is seen that the theoretical results and prediction results are compatible with each other in determining the output for new inputs and the reliability of the developed network is proved in different inputs according to this result. After that, experimentally not obtained variations of the heat transfer rate, overall heat transfer coefficient and energy efficiency are determined depending on the inlet temperatures and mass flow rate of cold water.
Highlights
Heat exchangers are often used for heat transfer between hot and cold fluids
The results show that the percentage errors for the temperatures of hot and cold waters predictions are less than 5% for all considered cases
A artificial neural network (ANN) is developed for prediction of the outlet temperatures of the chevron type heat exchanger depending on the inlet temperatures and mass flow rate of cold water when the mass flow rate of hot water is assumed to be constant for all cases
Summary
Heat exchangers are often used for heat transfer between hot and cold fluids. Heat exchangers used in a wide variety of engineering applications, such as automobile thermal power units, air conditioning systems, chemical and textile manufacturing operations, can be of various constructions, capacities, sizes and types according to their intended use. The plate types Chevron heat exchanger is selected because of its high thermal efficiency and flexibility. Chevron type heat exchangers are extensively preferred for heating, cooling and heat-regeneration applications. There are some theoretical and experimental methods for analysing the thermal behaviour of heat exchangers in the literature such as the logarithmic mean temperature difference (LMTD), effectiveness-number of transfer units (ε-NTU) and computational fluid dynamics (CFD)
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