Prediction of Grain Porosity Based on GWO-BP Neural Network and Grain Compression Experiment

Abstract

Porosity is a key parameter that affects the heat and moisture transfer within a grain pile. In order to investigate the distribution law of porosity in bulk grain piles in grain silos, grain compression experiment was carried out to obtain the porosity of different grain types under different moisture content and vertical pressure conditions. A porosity prediction model for grain cell based on GWO-BP neural network was proposed, and the model was compared with the porosity prediction results of BP neural network model and random forest model. Finally, the generalization ability of the model was verified using the grain cell box test. The results showed that the porosity prediction performance of the GWO-BP neural network model was the best, and the evaluation indexes of the model, including R2 of 0.960 5, RMSE of 0.013 7 and MAE of 0.013 1, were all within the permissible range. This study has provided a neural network prediction method for the determination of grain porosity, which could provide an important foundation for in-depth multi-field coupling analysis of grain piles and theoretical support for safe grain storage.

Keywords

• gwo-bp model
• grain porosity
• compression test
• prediction