JOURNAL ARTICLE

Design and parameters optimization for solid organic fertilizer loading and spreading machines based on the discrete element method.

  • Published In: Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.), 2024, v. 24, n. 6. P. 3477 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Liang, Xudong; Wang, Zehe; Yi, Jinggang 3 of 3

Abstract

The article focuses on the design and parameter optimization of an integrated solid organic fertilizer loading and spreading machine using the discrete element method (DEM). Addressing the challenges of manual fertilizer application, the study developed a machine capable of automating loading, transporting, and spreading processes, with simulations conducted in EDEM software to analyze fertilizer particle motion and spreading uniformity. Using Design-Expert software and a Box–Behnken experimental design, the research optimized key operational parameters—forward speed, spreading roller speed, and spreading height—resulting in an optimal combination of 0.42 m/s forward speed, 216 rpm roller speed, and 545 mm spreading height to enhance spreading uniformity. The findings provide a validated regression model and practical parameter recommendations to improve mechanization efficiency and uniformity in solid organic fertilizer application.

Additional Information

  • Source:Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.). 2024/11, Vol. 24, Issue 6, p3477
  • Document Type:Article
  • Subject Area:Nutrition and Dietetics
  • Publication Date:2024
  • ISSN:1472-7978
  • DOI:10.1177/14727978241299185
  • Accession Number:182615051
  • Copyright Statement:Copyright of Journal of Computational Methods in Sciences & Engineering (Sage Publications Inc.) is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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