One of proposed resource-saving technology varie-ties of complex preseeding seed treatment is realized by the cone classifier. In this improved classifier the seed material undergoes pre-sowing treatment, which in-cludes fractionation on flat sieves, impact and direction-al movement of particles, causing resonance condition. All listed occurs simultaneously. It is important to under-stand which limiting trajectories are performed by seed material particles and how affect them qualitatively by constructive and technological parameters of the classi-fier in order to improving the efficiency of the conducted process. At this stage of the study trajectories of sieved particles were studied when the working organ moved to the right. The distance between the sieves, the thick-ness of non-passing particle and the radius of the upper sieve were chosen as influencing parameters. By re-gression analysis, the basic statistical characteristics were determined and regression equations were made, which allow revealing active parameters significantly affecting the result. Obtained results of regression sta-tistics and dispersive analysis testify to high accuracy of the approximation. The reliability, determined by the importance of Fisher criterion, has a negligible value, allowing concluding that the model is significant. On the basis of values of coefficients of regression of entering sizes as active parameters appeared the distance be-tween the sieves and the radius of the top sieve. The thickness of treated seeds is practically unaffected by the magnitude of the trajectories if they are non-sieved. Preliminary conclusion of these researches is that for change of values of trajectories of not pro-running parti-cles it is necessary to vary in distance between sieves or to select the size of the top sieve, so, and all following behind it. The main conclusion can be drawn only after having investigated trajectories of pro-running particles which are a component of processed material.
cone classifier, limiting trajectory, non-sieved particle, upper sieve, regression analysis
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