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

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.