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Performance prediction of a cooling tower using artificial neural network

机译：基于人工神经网络的冷却塔性能预测

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摘要

This paper describes an application of artificial neural networks (ANNs) to predict the performance of a cooling tower under a broad range of operating conditions. In order to gather data for training and testing the proposed ANN model, an experimental counter flow cooling tower was operated at steady state conditions while varying the dry bulb temperature and relative humidity of the air entering the tower and the temperature of the incoming hot water along with the flow rates of the air and water streams. Utilizing some of the experimental data for training, an ANN model based on a standard back propagation algorithm was developed. The model was used for predicting various performance parameters of the system, namely the heat rejection rate at the tower, the rate of water evaporated into the air stream, the temperature of the outgoing water stream and the dry bulb temperature and relative humidity of the outgoing air stream. The performances of the ANN predictions were tested using experimental data not employed in the training process. The predictions usually agreed well with the experimental values with correlation coefficients in the range of 0.975-0.994, mean relative errors in the range of 0.89-4.64% and very low root mean square errors. Furthermore, the ANN yielded agreeable results when it was used for predicting the system performance outside the range of the experiments. The results show that the ANN approach can be applied successfully and can provide high accuracy and reliability for predicting the performance of cooling towers.
机译：本文介绍了人工神经网络（ANN）在预测冷却塔在各种运行条件下的性能时的应用。为了收集数据以训练和测试所提出的ANN模型，实验性的逆流冷却塔在稳态条件下运行，同时改变了干球温度和进入塔的空气的相对湿度以及沿塔的流入热水的温度空气和水流的流速。利用一些实验数据进行训练，开发了基于标准反向传播算法的神经网络模型。该模型用于预测系统的各种性能参数，即塔的排热率，蒸发到空气流中的水的速率，流出的水流的温度以及干燥球的温度和流出的相对湿度气流。使用训练过程中未使用的实验数据来测试ANN预测的性能。这些预测通常与相关系数在0.975-0.994范围内，平均相对误差在0.89-4.64％范围内以及极低的均方根误差的实验值非常吻合。此外，当将人工神经网络用于预测超出实验范围的系统性能时，其结果令人满意。结果表明，人工神经网络方法可以成功地应用，并且可以为预测冷却塔的性能提供高精度和可靠性。

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