中国烟草学报2025，Vol.31Issue(2)：58-65,8.DOI:10.16472/j.chinatobacco.2024.013
基于VMD-GRU的润叶过程片烟水分集成预测方法
Dynamic prediction method of tobacco leaf moisture in the leaf wetting process based on VMD and GRU network integration
摘要
Abstract
The moisture content of outlet tobacco leaves in the leaf wetting process is an important quality indicator.However,the wetting process is characterized by multivariable,nonlinear,and non-stationary features,posing significant challenges to moisture prediction.This study proposes an integrated prediction method based on Variational Mode Decomposition(VMD)and Gated Recurrent Units(GRU).First,VMD is utilized to decompose the moisture content of tobacco leaves,obtaining several Intrinsic Mode Functions(IMF).Then,GRU networks are established for modal components at different scales to extract multi-scale features.Concurrently,parallel GRU networks are designed to capture the complex temporal dependencies between process variables and tobacco leaf moisture.Finally,the hidden states output by all GRU networks are concatenated and further feature extraction and moisture prediction are performed through a fully connected layer.On a real production dataset from a rotary kiln factory,the results indicate that the VMD-GRU method improves the prediction accuracy by an average of 40%compared to traditional prediction methods,especially demonstrating significant precision advantages in multi-step predictions,thereby proving the algorithm's effectiveness and superiority.关键词
烟叶水分预测/变分模态分解/门控循环单元网络/润叶过程/软测量Key words
tobacco leaf moisture prediction/variational mode decomposition/gated recurrent units network/leaf moistening process/soft sensing引用本文复制引用
张雷,苏子淇,熊开胜,任国峰,洪斌斌,邹泉,郑红艳,赵云川,徐大勇,堵劲松,李银华..基于VMD-GRU的润叶过程片烟水分集成预测方法[J].中国烟草学报,2025,31(2):58-65,8.基金项目
国家烟草专卖局科研大数据重大科技项"卷烟制丝加工大数据关键技术研究与应用"(110202101083(SJ-07)) （110202101083(SJ-07）
河南省科技攻关计划项目(242102220028) （242102220028）
河南省博士后科研项目(202102087) （202102087）
