scholarly journals A multi-module supported energy efficient intelligent control system

Author(s):  
Doğan Başaran ◽  
Büşra Karanfil ◽  
Burak Tüysüz
2020 ◽  
pp. 38-54
Author(s):  
◽  
N. Zaiets ◽  
V. Shtepa ◽  
N. Gritsenko ◽  
◽  
...  

Now there is a need to improve the automated control system by creating an intelligent system that will comprehensively assess the technical and economic performance of the technological complex of multi-range production and form a plan for energy efficient management of technological equipment. To achieve this goal, it is advisable to use decision support systems based on the use of algorithms, procedures, methods of cognitive approach based on neural networks The purpose of the study is to develop information support for an intelligent dairy management system to ensure energy efficient use of technological equipment. The article presents the development of an intelligent control system for high-quality assortment production using an intelligent system for energy-efficient control of technological equipment, which quickly and efficiently in real time makes an optimal decision on the management of a dairy production complex and contributes to improving the efficiency of its functioning. The structure of the database of the intelligent system has been formed and the stages of creating an intelligent control system for the energy efficient use of technological equipment, which takes into account the peculiarities of the functioning of individual units of the technological complex, have been determined. The calculation of energy savings in the manufacture of certain types of products for a week was carried out to compare the energy efficiency of the enterprise according to the scenario set by the intelligent system and in normal mode.


2020 ◽  
pp. 81-86
Author(s):  
Yu.G. Kabaldin ◽  
D.A. Shatagin ◽  
M.S. Anosov ◽  
A.M. Kuz'mishina

The formation of chips during the processing of various materials was studied. The relationship between the type of chips, the type of crystal lattice of the material and the number of sliding systems is shown. A neural network model of chip formation is developed, which allows predicting the type of chips. An intelligent control system for the process of chip formation during cutting is proposed. Keywords: chip formation, crystal lattice, neural network model, type of chips. [email protected]


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