摘要
为实时了解箱式烘烤的箱体内部不同区域温湿度分布特征,基于深度学习方法,结合实际传感器监测的箱体内外温湿度数据,建立长短时记忆(LSTM)网络预测模型,并对学习速率和神经元节点数进行筛选,最后采用决定系数R2、均方根误差e_(RMSE)以及平均绝对误差百分比e_(MAPE)评估模型的预测性能。结果表明,当学习速率为0.008、神经元节点数为500时模型可达到较优性能;验证结果表明,各烘烤时期箱体内上中下层预测值与实测值变化趋势一致,预测值决定系数均在0.900以上;上中下层温度与相对湿度的预测值和真实值最大误差均出现在烘烤进行45 h至75 h之间,其中箱体中层误差最大,温度最大误差为1.87℃,相对湿度最大误差为12.54个百分点。本研究基于LSTM构建的温湿度预测模型能够较为准确地展现箱体内各层面的温度及相对湿度变化,可为箱式烘烤烤房环境温湿度的监测与调控提供一种方法。
In order to provide a method for real-time understanding of the temperature and humidity distribution characteristics in different regions within a box-type flue-cured tobacco production process,a Long Short-Term Memory(LSTM)network prediction model was established based on deep learning methods and in combination with historical temperature and humidity data monitored by actual sensors inside and outside the box.The learning rate and the number of neuron nodes were screened,and the model's prediction performance was evaluated using the coefficient of determination R2,root mean square error(eRMSE),and mean absolute percentage error(eMAPE).The results indicated that when the learning rate of the model is 0.008 and the number of neuron nodes is 500,the model can achieve the optimal performance.Through the verification of the prediction results,it was shown that the change trend of the predicted values in the upper,middle and lower parts inside the curing box during each baking period is consistent with the measured values,and the determination coefficients of the predicted values are above 0.900.The maximum error of the predicted value and the true value of the temperature and relative humidity in the upper middle and lower parts of the curing box’s interior appeared between 45 h and 75 h,of which the maximum error of the middle part of the box was the largest,the maximum error of the temperature was 1.87℃,and the maximum error of the relative humidity was 12.54 percentage points.The LSTM-based temperature and humidity prediction model proposed in this study can accurately depict the changes in temperature and relative humidity at various levels within the curing box,providing a method for monitoring and controlling the temperature and humidity in the box-type flue-curing barn environment.
作者
宋宗昊
闫伸
苏家恩
赵浩宾
宋正熊
路晓崇
遆晋松
郭瑞
SONG Zonghao;YAN Shen;SU Jia’en;ZHAO Haobin;SONG Zhengxiong;LU Xiaochong;TI Jinsong;GUO Rui(College of Tobacco Science,Henan Agricultural University,Zhengzhou 450046,China;College of Staff Education,China Tobacco Corporation,Zhengzhou 450008,China;Dali Tobacco Company of Yunnan Province,Dali 671000,Yunnan,China;Henan Province Company,China National Tobacco Corporation,Zhengzhou 450018,China)
出处
《中国烟草科学》
CSCD
北大核心
2024年第6期116-123,共8页
Chinese Tobacco Science
基金
河南省烟草公司资助项目(2023410000240027)
中国烟草总公司重点研发项目(110202102007)。
关键词
烤烟
箱式烘烤
温度
相对湿度
长短时记忆网络(LSTM)
flue-cured tobacco
box-type curing
temperature
relative humidity
long short-term memory(LSTM)
