Operation prediction of open sun drying based on mathematical-physical model, drying kinetics and machine learning

Abstract

In order to determine the moisture ratio of dried material whether the storage requirements are met, it was crucial to find an accurate prediction and convenient method in the open sun drying process. Therefore, the mathematical-physical model, drying dynamics model and machine learning methods were employed and compared in this study. The machine learning methods were first applied to predict the moisture ratio change of sweet potato during open sun drying. A large number of sweet potatoes drying experiments were carried out under open sun drying for theoretical analysis. The results shown that the drying kinetic model of sweet potato was also different under different drying climate conditions. The heat and mass transfer model of sweet potato was established and validated with R² 0.8990 and RMSE 0.0826. Different optimal machine learning prediction methods have be selected based on statistical metrics. Finaly, the machine learning prediction method was considered to be superior to the mathematical-physical model and the drying kinetic model in predicting moisture ratio. The results of this study can be analogized to drying process control of other agricultural products in the future.