基于多因素分析的烘丝机入口含水率预测模型的建立与应用

为了保障制丝过程中烘丝机入口含水率的稳定性,采用Pearson相关分析的方法,确定烘丝机入口含水率的主要影响因素,并用神经网络算法和多元回归分析方法建立含水率预测模型。通过模型求解,实现给定烘丝机入口含水率计算松散回潮机回潮加水比例参考值的目的。采用模型预测值与实测值对比的方法进行检验。结果表明:烘丝机入口含水率设定值为19.2%时,采用本方法得到的烘丝机入口含水率均值为19.21%,优于改进前的19.09%,且误差标准偏差由0.43%降到0.26%,批次间烘丝机入口含水率的波动得到改善。 

Establishment and Application of Multivariate Prediction Models for Moisture Content in Input Tobacco to Tobacco Dryer

To stabilize the moisture content in input tobacco to a tobacco dryer during primary processing, the major factors influencing its moisture content were determined by Pearson correlation analysis method, a prediction model for the moisture content was established by neural network algorithm and multivariate regression analysis. The reference value of water addition rate in conditioning process could be calculated on the basis of given moisture content in input tobacco to tobacco dryer via the prediction models. The predicted values were compared with the measured values, the results showed that when the moisture content in input tobacco was set at 19.2%, the mean value of moisture content in input tobacco obtained by this method was 19.21%, better than 19.09% before improvement; furthermore the standard deviation of error decreased from 0.43% to 0.26%. The deviation of moisture content in input tobacco to tobacco dryer between batches was narrowed.