Identification of Industrial and Innovative Development Models of Regional Economic Systems

New industrialisation challenges, turbulent economic environment and opening market niches change the structure of competitiveness factors and determine the innovativeness of industrial development. In the current context, it is necessary to deepen the analysis of industrialisation and innovation performance of regions. Therefore, this study aims to identify industrial and innovative development models present in Russian regions. To this end, we propose a methodology based on assessing the localisation coefficients of both regional industrialisation and innovation performance. Calculation of these indicators resulted in the creation of four models: Model 1 (low industrial development and low innovation performance), Model 2 (low industrial development and high innovation performance), Model 3 (high industrial development and high innovation performance), Model 4 (high industrial development and low innovation performance). The classification of the constituent entities of the Russian Federation according to the industrial and innovative development model shows that more than 40 % of regions use Model 1 and about 12 % of territories use Model 2. Simultaneously, approximately 27 % of regions (including Tula, Lipetsk, Chelyabinsk, Vladimir oblasts, Republic of Bashkortostan) chose Model 3, which most fully meets the new industrialisation challenges. The high stability of this disproportionate structure indicates the absence of positive dynamics and poor balance of industrial and innovation policy measures in most Russian regions in the period 2015–2019. The study results can be used to create an alternative ranking of innovative development of regions. Further research can apply these findings to assess the efficiency of regional industrial and innovation policies

Identification of Industrial and Innovative Development Models of Regional Economic Systems

New industrialisation challenges, turbulent economic environment and opening market niches change the structure of competitiveness factors and determine the innovativeness of industrial development. In the current context, it is necessary to deepen the analysis of industrialisation and innovation performance of regions. Therefore, this study aims to identify industrial and innovative development models present in Russian regions. To this end, we propose a methodology based on assessing the localisation coefficients of both regional industrialisation and innovation performance. Calculation of these indicators resulted in the creation of four models: Model 1 (low industrial development and low innovation performance), Model 2 (low industrial development and high innovation performance), Model 3 (high industrial development and high innovation performance), Model 4 (high industrial development and low innovation performance). The classification of the constituent entities of the Russian Federation according to the industrial and innovative development model shows that more than 40 % of regions use Model 1 and about 12 % of territories use Model 2. Simultaneously, approximately 27 % of regions (including Tula, Lipetsk, Chelyabinsk, Vladimir oblasts, Republic of Bashkortostan) chose Model 3, which most fully meets the new industrialisation challenges. The high stability of this disproportionate structure indicates the absence of positive dynamics and poor balance of industrial and innovation policy measures in most Russian regions in the period 2015–2019. The study results can be used to create an alternative ranking of innovative development of regions. Further research can apply these findings to assess the efficiency of regional industrial and innovation policies

Modeling of milled straw heap separation in air-flow classificator with three pneumatic ducts

The research subject is a multivariate analysis of the air-flow classification process of the straw heap with a predetermined fractional composition supplied to the cleaning after the drum destroyer. The work objective is to identify patterns of the air separation using three pneumatic ducts with a linkage parameter variation. The investigative technique is an analytical modeling. The effect of probabilistic characteristics of the heterogenic thrashed heap supply, airflow velocity distribution by the separator width, and density function of heap components terminal velocity on the separation is evaluated. The air-flow classification process on each section of the pneumatic duct is considered. To this end, the mathematical expressions averaging the air classification indicators of the consistently functioning three pneumatic ducts and a stochastic quasistatic mathematical model of the separator operation with three pneumatic ducts in series are used. The results of the separator parametric synthesis and its technological parameters are presented. The fractional constituents of the heap components and their percent-sizes in each air-classified fraction are shown. The possibility of the air-flow classification of the crushed straw heap in the predetermined factions at the preset productivity of 0.6-0.7 kg/m∙s is revealed. It is found that under the rational functioning of the air separator, the straw content in the service faction is 97.03%. In this case, the minimum grain content is 0.03%, and mineral impurities are 0%. It is revealed that the air separator with three pneumatic ducts in series is sufficient for separating the crushed straw heap with the predetermined agro-technical requirements

SIMULATION OF GRAIN PILE SEPARATION IN VERTICAL PNEUMATIC PASSAGE

The methodology and grain pile pneumatic separation parameters in the vertical pneumatic passage under the specified probabilistic characteristics of the airflow, the grain pile feed into the pneumatic separator, and the terminal velocity probability density of all pile components are described