冷却塔出塔水温预测方法的对比分析及应用
Comparative analysis and application of cooling tower outlet water temperature prediction methods
摘要：
针对传统的BP神经网络预测方法存在的缺陷，提出了一种经遗传算法优化的BP神经网络预测方法。基于实测数据，比较分析了焓差法、传统BP神经网络法、优化BP神经网络法3种预测方法，并分析了循环水流量、进塔水温、湿球温度、干球温度对冷却塔出塔水温的影响。结果表明，运用优化BP神经网络法得到的出塔水温均方误差为0.000 787 ℃2，平均相对误差为0.079 9%，均方根误差为0.028 069 ℃，明显小于其他预测方法。使用改进后的预测模型对某公司的开式冷却塔全年运行数据进行分析，发现出塔水温与循环水流量、进塔水温、湿球温度呈正相关关系，与干球温度呈负相关关系。
Abstract：
Aiming at the defects of the traditional BP neural network prediction method, proposes a genetic algorithm optimized BP neural network prediction method. Based on the measured data, compares and analyses the three prediction methods of enthalpy difference method, traditional BP neural network method and optimized BP neural network method, and analyses the effects of circulating water flow, inlet water temperature, wet bulb temperature and dry bulb temperature on the cooling tower outlet water temperature. The results show that the mean square error of the tower outlet water temperature obtained by the optimized BP neural network method is 0.000 787 ℃2, the average relative error is 0.079 9%, and the root mean square error is 0.028 069 ℃, which is significantly smaller than other prediction methods. Using the improved prediction model to analyse the annual operating data of a company’s open cooling tower, it is found that the outlet water temperature is positively correlated with the circulating water flow, the inlet water temperature and the wet bulb temperature, and negatively correlated with the dry bulb temperature.
Keywords:cooling tower, outlet water temperature, genetic algorithm, BP neural network, enthalpy difference method