摘要
研究小麦真空干燥工艺参数(真空度、加热温度、铺料厚度)对含水率的影响规律,当小麦含水率较高时,提高真空度和加热温度、减小铺料厚度能显著提高干燥速率。利用全部试验数据建立了真空干燥各工艺参数与小麦含水率之间的BP(Back Propagation)神经网络预测模型。验证结果表明:小麦含水率的预测结果与实测值误差小于5.2%,所建立的BP神经网络模型能较好地反映真空干燥工艺参数与含水率之间的复杂非线性关系。
The present study was to establish the prediction model of water content of wheat in vacuum drying system based on BP neural network. The influence regulation of the process parameters, including vacuum degree, drying temperature, wheat thickness on the water content of wheat was obtained, which was the higher vacuum degree and drying temperature, lower wheat thickness could improve the drying speed apparently. Based on these work, a BP neural network model was established and trained with data from experiments in order to forecast the drying process. The predicted result was correspond to experimental result, which indicated that BP neural network model established in this study could predict the water content of wheat under given process parameters. The present study provided theoretical basis for the prediction of the vacuum drying performance of wheat.
出处
《河南工业大学学报（自然科学版）》
CAS
北大核心
2016年第3期101-106,共6页
Journal of Henan University of Technology：Natural Science Edition
基金
"863"课题项目(2011AA100802)
河南省科技厅产学研项目(142107000089)
河南省科技攻关项目(162102210204)
关键词
BP神经网络
真空干燥
含水率
预测
BP neural network
vacuum drying
water content
prediction
