Improving Energy Efficiency of Grain Cleaning Technology

The relevance of the research topic was substantiated, and the purpose and objectives of the study were formulated. It was determined that the energy efficiency of the grain cleaning process can be evaluated only when all the machines operate in a single production line and are coordinated in productivity. Research on energy savings and energy efficiency in the technological process of post-harvest grain treatment using grain cleaning units by implementing the method of mathematical experiment planning (MEP) was conducted. The method is based on transforming the initial mathematical models of various research objects into a model in the form of regression equations convenient for solving the problems of research, analysis and optimization of the object. For the first time, the mathematical description of the target function was performed by conducting a multifactorial experiment based on second-order design. The research produced adequate second-order regression equations, making it possible to determine the minimum specific consumption of electricity for the technological process of post-harvest grain treatment at grain cleaning facilities, while complying with the agrotechnical requirements for the quality of the cleaned grain. The problem of optimization of the research object was solved by mathematical transformations, which enabled obtaining graphical and analytical interpretations of the optimum area. For this purpose, the canonical transformation of the mathematical model and the method of two-dimensional cross-sections of the response surface were used. Research has led to the development of scientifically based standards of specific electricity consumption for grain cleaning processing schemes, which serve as criteria for evaluating energy savings in the technological processes.

Automated summary

This study examines the energy savings and efficiency in the post-harvest grain treatment process using grain cleaning units. Through mathematical experiment planning, the study establishes regression equations and identifies the optimum area for energy consumption. The results are significant for developing energy-saving standards in grain processing schemes.