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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 attractiveness of using expert knowledge is obvious, since it uses the experience of highly qualified specialists in this area of knowledge, does not require additional time and money to conduct expensive experiments. However, this approach requires the use of special methods of convolution of expert knowledge (EK) in the multifactor space for solving practical problems. An example of constructing a fuzzypossibility model in a fivedimensional factor space for evaluating a generalized indicator of the efficiency of a technological process for the production of forages from grass is considered. The factors selected by the expert, in turn, are aggregated, i.e., generalized by factors of a lower level of the hierarchy, and constitute a virtual space. Nevertheless, this approach provides an opportunity from a single position and in a single factor space to carry out comparative assessments of the technologies under study. It is shown that the results of quantitative assessment according to the generalized indicator of the effectiveness of technologies completely coincide with the true states of forage production from grasses in farms, depending on the technologies they use – from highintensity to basic ones. The adequacy of calculations for the proposed fuzzypossibility model (NVM) to the actual states of various applied technologies allows us to recommend the considered approach when assessing the state of forage production from grasses for use in agriculture in the NorthWest region of the Russian Federation.