Experimental analysis of drying parameters and material properties on the moisture content, energy consumption, and microstructure of aquafeed pellets

Thermal efficiency and optimal drying processes are critical to the quality of dried aquafeed. However, optimizing these processes is challenging due to the complex interplay between material properties and drying parameters. This study presents a comprehensive experimental investigation on the drying behavior of ten types of aquafeed varying in particle size and bulk density. Drying experiments were conducted under different air temperatures and velocities to analyze the effects on moisture content (MC), effective moisture diffusivity (Deff), and energy consumption (EC). The results demonstrated that drying air velocity and feed density had a more significant impact on the drying rate and EC than drying temperature. Furthermore, the evolution of the microstructure observed by Scanning Electron Microscopy (SEM) revealed how porosity affects drying outcomes at the microscale. Based on the experimental data, a Physical Model Correction-Back Propagation (PMC-BP) neural network model was developed to predict the MC and EC throughout the drying cycle. The model achieved a high prediction accuracy (R2= 0.97), validating the experimental findings. The thermal efficiency analysis provided insights for optimizing drying stages. This study offers a valuable database and mechanistic insights for optimizing industrial aquafeed drying processes, with the PMC-BP model serving as an effective tool for prediction and analysis.