Moisture Content Prediction Modeling of Hot-air Drying for Pressed Peony Based on BP Neural Network

Abstract:

Pressed peony was made by hot-air drying method. The influence of temperature of hot-air, speed of hot-air, drying board's hole density and the initial mass of peony on drying speed was discussed. Relationship model between drying time, temperature of hot-air, speed of hot-air, drying board's hole density, the initial mass and moisture content was built by using BP neural network. Parameters in the proposed model were trained and simulated in Matlab. The results indicated that the simulated values of the drying moisture content were close to the measured values.