MATHEMATICAL MODELS FOR NORMALIZING GRAIN PILES ON CONTAMINATION AND MOISTURE CONTENT AND THE TECHNOLOGY OF ITS PRELIMINARY CLEANING AND DRYING
In the structure of grain production costs in wet areas, post-harvest operations costs, which include drying of grain heap, reach 50%. Therefore, the development of technologies and techniques designed to reduce the energy intensity of drying of seeds and grains, is an important and topical issue. Many scholars, both in our country and abroad, deal with the solution to this problem in the direction of improving technology of preliminary cleaning and drying, the dryer constructions. Development of mathematical models that take into account the probabilistic and statistical nature of conditions of post-harvest grain items and random nature of the technological operations related to the separation of components will lead to the creation of new technologies and equipment for seed preparation. The objective of our research was the development of the mathematical models of normalizing grain heap on contamination and moisture and, on that basis, the improvement of technology of preliminary cleaning and drying of grain and seeds. As a result, we obtained the mathematical models to predict the technology and energy evaluation of preliminary cleaning and drying of grain heap. To improve the performance of the production lines of the grain pile on normalization of humidity and contamination and reduce energy costs for their work, a two-stage technology of pretreatment of grain heap by splitting into factions with their differentiated heat treatment. If you use the second-stage pre-treatment cylindrical sieve diameter 1200 mm, with the average value of supply 14200 kg/h of barley grain pile with 22.3% of humidity, the rotation rate must not be more than 201/min. The developed nomogram allows the choice of sizes of holes in sieves at a certain fullness of separation, separation of wet forage fractions over a specified range to its preparation using different technologies to feed animals.