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DESIGN OF BIDIRECTIONAL SCREW EXTRUSION BIOMASS HONEYCOMB BRIQUETTE MACHINE.

  • Published In: International Journal of Mechatronics & Applied Mechanics, 2025, n. 19. P. 12 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Ning Tingzhou; Zhang, Yue; Qian Xuewu; Zhang Lei 3 of 3

Abstract

Biomass densification machines can compress crushed crop straw and other agricultural residues into biomass densified fuels with a specific shape and density under certain external force, temperature, and humidity conditions. Existing biomass densification machines primarily produce solid biomass fuels in the form of rods, blocks, and pellets. In order to improve the moulding efficiency of biomass briquette machines and increase the effective contact area between biomass fuel and oxygen during combustion, a bidirectional screw extrusion biomass honeycomb briquette machine is designed. The briquette machine consists of two main components, the screw extrusion device and the moulding device. The screw extrusion device facilitates continuous material transport via a screw, while the moulding device compresses the crushed biomass material into a honeycomb shape within the moulding sleeve through a crank-slider mechanism. Additionally, since the densification machine compresses the biomass material from two directions, it enhances the production efficiency of biomass densified fuels. The design of this briquette machine is of significance for promoting the widespread use of biomass densified fuels. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Mechatronics & Applied Mechanics. 2025/01, Issue 19, p12
  • Document Type:Article
  • Subject Area:Physics
  • Publication Date:2025
  • ISSN:2559-4397
  • DOI:10.17683/ijomam/issue19
  • Accession Number:184620216
  • Copyright Statement:Copyright of International Journal of Mechatronics & Applied Mechanics is the property of Romanian Review Precision Mechanics, Optics & Mecatronics 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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