Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions |
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Authors: | Ming Gao Feng-zhong Sun Shou-jun Zhou Yue-tao Shi Yuan-bin Zhao Nai-hua Wang |
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Affiliation: | 1. School of Energy and Power Engineering, Shandong University, Jinan 250061, China;2. School of Mechanical and Mining Engineering, The University of Queensland, QLD 4072, Australia;3. School of Mechanical Engineering, Chongqing University of Technology, Chongqing 400054, China;1. School of Energy Source and Power Engineering, Shandong University, Jinan 250061, China;2. Shandong Electric Power Engineering Consulting Institute, Jinan, 250014, China;1. Mechanical Engineering Department, University of Shahrekord, Shahrekord 88186-34141, Iran;2. Department of Mechanical Engineering, Isfahan University of Technology, Isfahan 84156-83111, Iran;3. Isfahan Mathematics House, Isfahan 81698-51177, Iran |
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Abstract: | This paper describes an application of artificial neural networks (ANNs) to predict the thermal performance of a cooling tower under cross-wind conditions. A lab experiment on natural draft counter-flow wet cooling tower is conducted on one model tower in order to gather enough data for training and prediction. The output parameters with high correlation are measured when the cross-wind velocity, circulating water flow rate and inlet water temperature are changed, respectively. The three-layer back propagation (BP) network model which has one hidden layer is developed, and the node number in the input layer, hidden layer and output layer are 5, 6 and 3, respectively. The model adopts the improved BP algorithm, that is, the gradient descent method with momentum. This ANN model demonstrated a good statistical performance with the correlation coefficient in the range of 0.993–0.999, and the mean square error (MSE) values for the ANN training and predictions were very low relative to the experimental range. So this ANN model can be used to predict the thermal performance of cooling tower under cross-wind conditions, then providing the theoretical basis on the research of heat and mass transfer inside cooling tower under cross-wind conditions. |
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