BP神经网络模型对浮法退火窑内玻璃温度的预测  

Temperature Prediction for the Float Glass Ribbon in Lehr Based on BP Neural Network

作　　者：朱锦杰[1] 童树庭[1] 郭凤姣 

机构地区：[1]同济大学材料科学与工程学院,上海200092 [2]中国洛阳浮法玻璃集团有限责任公司,河南洛阳471009

出　　处：《建筑材料学报》2006年第3期377-380,共4页Journal of Building Materials

摘　　要：利用热电偶测得的退火窑中空气温度和红外测温仪测得的玻璃表面温度作为训练样本,建立了基于BP神经网络的玻璃温度预测模型.通过与实际工况对比,证实了该模型的有效性.该模型对改善玻璃退火窑运行质量,预测玻璃成型性能具有积极的意义.By using the air temperature measured by thermocouples and the glass temperature measured by infrared thermometer as the training sample, a model for prediction of the temperature parameters for the float glass in lehr was created based on the ameliorated BP neural network. The results of simulation show that the model is effective and feasible. It can improve the quality of the annealing process and diagnose some failures.

关 键 词：BP神经网络 浮法玻璃 退火温度 退火窑 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TQ171.64[自动化与计算机技术—控制科学与工程]