摘要
目的明确温湿度对松散回潮工序水分变化量的影响。方法使用K-means聚类分析法划分张家口市环境温湿度区间,利用梯度提升决策树筛选松散回潮水分变化量相关的工艺参数作为特征变量,构建不同温湿度区间下松散回潮工序水分变化量的机器学习预测模型,同时探究车间温湿度对水分变化量的影响,并对最终模型进行优化。结果根据外界环境全年的温湿度,可划分为低温中湿、中温低湿、高温高湿、中温中湿4个区间,且中温低湿与高温高湿区松散回潮工序水分变化量的差异最为显著;分别对中温低湿和高温高湿区构建松散回潮工序水分变化量的LSR、SVR、DT、RF预测模型,综合对比可知,SVR、RF预测模型的精度最高;将车间温湿度作为特征变量后,松散回潮工序水分变化量SVR、RF模型的预测精度均有所上升,其R^(2)'(R^(2)与1的差值)分别降低了25%、46%,说明车间温湿度对松散回潮工序水分变化量的影响较大;对中温低湿和高温高湿区SVR、RF松散回潮工序水分变化量预测模型进行优化后,最终模型的R^(2)'分别达到0.08、0.04。结论松散回潮水分变化量受到外界和车间温湿度的影响,根据不同温湿度区间构建的中温低湿RF模型和高温高湿SVR模型对水分变化量的拟合效果较好,预测准确,能够可靠地应用于松散回潮工序水分变化量的预测,且可拓展用于纸质卷烟包装材料的水分预测和控制,对提升烟丝的卷接包装质量具有重要的理论意义和实际应用价值。
The work aims to clarify the impact of temperature and humidity on the moisture change in loosening and conditioning process.K-means clustering analysis was used to divide the environmental temperature and humidity range in Zhangjiakou City and gradient enhancement decision tree was used to select parameters related to the moisture change in loosening and conditioning as characteristic variables.The prediction model based on machine learning was constructed for the moisture change in loosening and conditioning under different temperature and humidity ranges.Simultaneously,the impact of workshop temperature and humidity on moisture change was explored and the final model was optimized.According to the temperature and humidity of the external environment,it could be divided into four ranges throughout the year,including low temperature and medium humidity,medium temperature and low humidity,high temperature and high humidity,and medium temperature and medium humidity.The difference of moisture change in loosening and conditioning between the medium temperature and low humidity range and the high temperature and high humidity range was the most significant.Least Squares Regression,Support Vector Regression,Decision Tree and Random Forest prediction models were constructed for the moisture change in loosening and conditioning in the ranges of medium temperature,low humidity,and high temperature,high humidity.Through comprehensive comparison,it was found that the SVR and RF prediction models had the highest accuracy.After adoption of workshop temperature and humidity as the characteristic variable,the prediction accuracy of the SVR and RF models for the moisture change in loosening and conditioning increased,with R^(2)´(difference between R^(2)and 1)respectively decreasing by 25%and 46%,indicating that the temperature and humidity in the workshop had significant impact on the moisture change in loosening and conditioning process.After optimization of prediction models of the SVR and RF for moisture change in the medi
作者
李自娟
李宜馨
赵海洋
陈娇娇
吕萱
孙朔
方世航
李晓
LI Zijuan;LI Yixin;ZHAO Haiyang;CHEN Jiaojiao;LYU Xuan;SUN Shuo;FANG Shihang;LI Xiao(Zhangjiakou Cigarette Factory Co.,Ltd.,Hebei Zhangjiakou 075000,China;College of Tobacco Science and Engineering,Zhengzhou University of Light Industry,Zhengzhou 450002,China)
出处
《包装工程》
CAS
北大核心
2024年第17期119-128,共10页
Packaging Engineering
基金
河北中烟工业有限责任公司科技项目(HBZY2023A040,AW201911)。
关键词
温湿度
松散回潮
水分变化量
机器学习
temperature and humidity
loosening and conditioning
moisture change
machine learning
