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A reversibly used cooling tower with adaptive neuro-fuzzy inference system

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Abstract

An adaptive neuro-fuzzy inference system (ANFIS) for predicting the performance of a reversibly used cooling tower (RUCT) under cross flow conditions as part of a heat pump system for a heating mode in winter was demonstrated. Extensive field experimental work was carried out in order to gather enough data for training and prediction. The statistical methods, such as the correlation coefficient, absolute fraction of variance and root mean square error, were given to compare the predicted and actual values for model validation. The simulation results predicted with the ANFIS can be used to simulate the performance of a reversibly used cooling tower quite accurately. Therefore, the ANFIS approach can reliably be used for forecasting the performance of RUCT.

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Authors and Affiliations

  1. College of Civil Engineering, Hunan University, Changsha, 410082, China

    Jia-sheng Wu  (吴加胜), Guo-qiang Zhang  (张国强), Quan Zhang  (张泉), Jin Zhou  (周晋), Yong-hui Guo  (郭永辉) & Wei Shen  (沈炜)

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  1. Jia-sheng Wu  (吴加胜)
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  2. Guo-qiang Zhang  (张国强)
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  3. Quan Zhang  (张泉)
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  4. Jin Zhou  (周晋)
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  5. Yong-hui Guo  (郭永辉)
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  6. Wei Shen  (沈炜)
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Corresponding author

Correspondence to Jia-sheng Wu  (吴加胜).

Additional information

Foundation item: Projects(51108165, 51178170) supported by the National Natural Science Foundation of China

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Wu, Js., Zhang, Gq., Zhang, Q. et al. A reversibly used cooling tower with adaptive neuro-fuzzy inference system. J. Cent. South Univ. Technol. 19, 715–720 (2012). https://doi.org/10.1007/s11771-012-1062-x

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  • DOI: https://doi.org/10.1007/s11771-012-1062-x

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