基于1D-CNN的稻谷石墨烯远红外干燥模型及含水率在线预测OA北大核心

[Objectives]To achieve precise prediction of the moisture ratio during the far-infrared drying process of rice grains,a rice drying moisture ratio prediction model based on one-dimensional convolutional neural network(1D-CNN)had been proposed,enabling online prediction of the moisture content of rice grains during drying.[Methods]After standardizing the initial moisture content of rice grains,drying experiments at various temperatures were conducted on a homemade graphene far-infrared drying experimental platform.A set of eight process parameter data,including drying temperature and humidity,was collected every 2 minutes and subjected to normalization treatment to form the dataset.The 1D-CNN drying model was constructed with the eight process parameters as inputs and the moisture ratio as output.The model parameters were determined through training,and the model was validated and compared with six classic thin-layer drying models and four typical machine learning drying models.[Results]Experimental results demonstrated that the proposed 1D-CNN drying model was capable of accurately depicting the variation of moisture ratio during the drying process.The determination coefficient(R2),root mean square error(RMSE),and mean absolute error(MAE)were achieved at 0.993 1,0.018 9,and 0.012 1,respectively.The MAE and mean relative error(MRE)for moisture content prediction were 0.143 2%and 0.007 8%,respectively,significantly outperforming the other compared drying models.[Conclusions]The proposed 1D-CNN drying model could accurately predict the changes in moisture content during the rice drying process,fully meeting the requirements for online moisture content detection.