• 基础研究 •

基于BP神经网络预测红外-喷动干燥带壳鲜花生水分比

1. （1.河南科技大学食品与生物工程学院，河南 洛阳 471000；2.粮食储藏安全河南省协同创新中心，河南 郑州 450001）
• 出版日期:2022-06-15 发布日期:2022-06-30
• 基金资助:
国家自然科学基金面上项目（31671907）；“十三五”国家重点研发计划重点专项（2017YFD0400901）； “智汇郑州·1125聚才计划”项目（郑政[2017]40号）；河南省高校重点科研项目（20A550006）

Backward Propagation (BP) Neural Network-Based Prediction of Moisture Ratio of Fresh In-shell Peanut during Infrared-Assisted Spouted Bed Drying

ZHU Kaiyang, REN Guangyue, DUAN Xu, QIU Caixia, LI Linlin, CHU Qianqian, YU Zuyan

1. (1. College of Food and Bioengineering, Henan University of Science and Technology, Luoyang 471000, China; 2. Henan Collaborative Innovation Center for Grain Storage Security, Zhengzhou 450001, China)
• Online:2022-06-15 Published:2022-06-30

Abstract: In order to predict the moisture ratio of in-shell peanut during infrared-assisted spouted bed drying, the effects of drying temperature (55, 60, 65 and 70 ℃), inlet airflow rate (16, 17, 18 and 19 m/s) and the amount of glidant (1.0, 1.5, 2.0 and 2.5 kg) on the drying time and drying rate were investigated. A BP neural network model whose topological structure was “4-11-1” was established using drying temperature, inlet airflow rate, the amount of glidant and drying time as the input layers, and the moisture ratio of peanut as the output layer. The number of nodes in the hidden layer of the established network was 11. The results showed that drying temperature and inlet airflow rate were the main factors affecting the moisture ratio of fresh in-shelled peanut. Increasing the inlet airflow rate or drying temperature could effectively shorten the drying time and improve drying efficiency. Using the Levenberg-Marquardt (LM) algorithm and tansig-purelin as the training function and the network transfer function, respectively, the BP neural network model was obtained after finite training. The determination coefficient (R2) between the predicted value and the experimental value was 0.99, and the mean square error was 0.02. Compared with the traditional classical model, the BP neural network model allowed rapid and accurate prediction of the moisture ratio. This model can provide a theoretical basis and technical support for on-line prediction of moisture ratio of fresh in-shell peanut during infrared-assisted spouted bed drying.