卷烟制丝环节关键工序水分预测模型的建立与检验

以卷烟制丝环节的松散回潮工序、加料回潮工序、热风润叶工序以及制丝全线为研究对象,利用人工神经网络及多元回归建模方法,考察不同建模方法对各工序水分预测精度的影响,并对其进行运行测试。结果表明:松散回潮工序水分预测选择多元回归方法建模,其预测误差绝对值的均值为0.24%;加料回潮工序水分预测选择人工神经网络方法建模,其预测误差绝对值的均值为0.20%;热风润叶工序水分预测选择人工神经网络方法建模,其预测误差绝对值的均值为0.10%;制丝全线水分预测选择人工神经网络方法建模,其预测误差绝对值的均值为0.05%;模型运算系统基于C#语言开发,使用SQLSERVER数据库存储数据;开发的模型运算系统具有很强的数据分析能力和生产预测能力,可用于卷烟制丝环节各关键工序的水分预测。

Establishment and detection of moisture prediction model of key processes of cigarette cutting process

Taking the loosening and conditioning process, feeding moisture returning process, hot air moistening process and the whole silk making process as the research objects, the artificial neural network and multiple regression modeling method were used to investigate the influence of different modeling methods on the moisture prediction accuracy of each process. moreover, the predicted results were tested experimentally. The experimental results showed that the mean of the absolute value of the prediction error was 0.24% for the loose moisture returning process with multiple regression modeling method. The mean of the absolute value of the prediction error was 0.20% for feeding moisture returning process with artificial neural network method. The mean of the absolute value of the prediction error was 0.10% for hot air moistening process with artificial neural network method. The mean of the absolute value of the prediction error was 0.05% for all the silk making process with artificial neural network method. The model computing system was developed based on C# language, with the use of SQLSERVER database for data storage. The developed model operation system had strong ability of data analysis and production prediction, which could be used to predict the moisture content of each key process in cigarette silk making process.