Numerical simulation of shower cooling tower based on artificial neural network

• Published in: Energy conversion and management Vol. 49; no. 4; pp. 724 - 732
• Main Authors: Qi, Xiaoni, Liu, Zhenyan, Li, Dandan
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.04.2008; Elsevier
• Subjects: Applied sciences; Devices using thermal energy; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Heat and mass transfer; Heat exchangers (included heat transformers, condensers, cooling towers); Neural network; Shower cooling tower; Shower cooling tower; Heat and mass transfer; Neural network; Salt; Experimental test; One dimensional model; Heat mass transfer; Numerical simulation; Cooling tower
• ISSN: 0196-8904; 1879-2227
• DOI: 10.1016/j.enconman.2007.07.032

This study was prompted by the need to design towers for applications in which, due to salt deposition on the packing and subsequent blockage, the use of tower packing is not practical. The cooling tower analyzed in this study is void of fill, named shower cooling tower (SCT). However, the present study focuses mostly on experimental investigation of the SCT, and no systematic numerical method is available. In this paper, we first developed a one dimensional model and analyzed the heat and mass transfer processes of the SCT; then we used the concept of artificial neural network (ANN) to propose a computer design tool that can help the designer evaluate the outlet water temperature from a given set of experimentally obtained data. For comparison purposes and accurate evaluation of the predictions, part of the experimental data was used to train the neural network and the remainder to test the model. The results predicted by the ANN model were compared with those of the standard model and the experimental data. The ANN model predicted the outlet water temperature with a MAE (mean absolute error) of 1.31%, whereas the standard one dimensional model showed a MAE of 9.42%.