Optimizing Grain Milling via Python Simulation and Experimental Validation: Effect of Technical Clearance and Grain Physics
收藏NIAID Data Ecosystem2026-05-10 收录
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https://data.mendeley.com/datasets/kr3m7ntwx4
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资源简介：
• Integrated a Python-based simulation with experimental validation to optimize hammer mill performance for corn and barley.
• Identified a 4 mm technical clearance as the universal optimum for maximizing the 500–1000 µm target particle fraction.
• Established a high-fidelity correlation (R2 = 0.96) between physics-based simulation models and empirical milling data.
• Demonstrated that grain mechanical resistance (e.g., 240.5 N for barley) significantly dictates clearance sensitivity and energy efficiency.
• Provided a robust engineering tool for precision milling automation and reducing specific energy consumption in feed production.
创建时间：
2026-01-19
