The chemical components and water content are related to the physical and mechanical properties of straw, affecting the processing technology that utilizes straw as energy and feed. The ratio of the viscous component to the elastic component, denoted as q, characterizes different chemical components of alfalfa. The influence of eigenvalue q on the densification of alfalfa particles was analyzed. By examining the morphology of alfalfa briquettes, the main binding mode of particles in the alfalfa briquettes was identified as the solid bridge formed by viscous components. A GA-BP-ANN artificial neural network prediction model was established to predict energy consumption and alfalfa briquette quality using q, improving the model's accuracy and stability while reducing the number of iterations. The research results contribute to determining the appropriate compression process for alfalfa, thereby promoting the efficient and high-quality utilization of alfalfa.
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