Prediction of moisture content and energy consumption in microwave drying of beef based on an optimized SSA-BP model

This study investigates the application of an enhanced Back-Propagation (BP) neural network model for analyzing and predicting beef microwave drying processes. Based on Fick’s second law of diffusion, effective moisture diffusivity was determined under varying microwave power levels (70-420 W) and relative humidity conditions (0%, 30%, 50%). Experimental results revealed moisture diffusivity values ranging from 2.23×10^–9 to 2.87×10^–8 m²/s. A significant inverse relationship was observed between microwave power and specific energy consumption, with optimal energy efficiency (8.39 MJ/kg water) achieved at 420 W. A multi-layer BP neural network architecture was developed to model drying kinetics and energy consumption patterns, with subsequent optimization using Sparrow Search Algorithm (SSA) for weight and threshold parameter calibration. Comparative analysis demonstrated that the SSA-optimized BP neural network significantly outperformed both conventional BP models and genetic algorithm-optimized variants in predictive accuracy. The enhanced model exhibited robust performance in predicting moisture content evolution and energy consumption dynamics throughout the drying process. These findings provide valuable insights for developing energy-efficient industrial-scale beef drying systems while maintaining product quality. The proposed intelligent computing framework represents a promising approach for precise modeling, prediction, and optimization of microwave drying processes in food processing applications.