| Issue |
E3S Web of Conf.
Volume 222, 2020
International Scientific and Practical Conference “Development of the Agro-lndustrial Complex in the Context of Robotization and Digitalization of Production in Russia and Abroad” (DAIC 2020)
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|---|---|---|
| Article Number | 01020 | |
| Number of page(s) | 6 | |
| Section | Creation and use of Modern Digital, Intelligent, Robotic Systems and Technologies, New Materials and Methods of Construction, Big Data Processing and the Internet of Things in the Agro-Industrial Complex | |
| DOI | https://doi.org/10.1051/e3sconf/202022201020 | |
| Published online | 22 December 2020 | |
Rotary-centrifugal shredder for forage preparation
1 Federal State Budgetary Scientific Institution “Federal Scientific Agroengineering Center VIM”, branch in 3, Filtrovskoje Shosse p.o. Tiarlevo, 196625 Saint Petersburg, Russia
2 Federal State Budgetary Educational Institution of Higher Professional Education Vologda State Dairy Farming Academy by N.V.Vereshchagin, 160555, 2, Shmidta st., Molochnoe, Vologda, Russia
* Corresponding author: papushin@sznii.ru
Existing grain shredders have disadvantages, the main of which are the following: high metal and energy consumption, uneven granulometric composition of the crushed product, high percentage of pulverized fraction, rapid wear of working bodies, product heating. To eliminate the disadvantages, the design of a rotary-centrifugal grain shredder is proposed. The presented design requires optimization of a number of structural and kinematic parameters. To solve this problem, the method of a multifactor experiment was chosen. The feed of grain material, the rotor speed, the opening of the separating surface, and the number of knives on the inner and outer rings were taken as factors that varied at two levels. As optimization criteria, the plant performance, power consumption during grain grinding, and compliance of the resulting product with zootechnical requirements were considered. As a result of data array processing, adequate and reliable mathematical models were obtained. As a result of model analysis, the influence of factors on optimization criteria was established.
© The Authors, published by EDP Sciences, 2020
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