Development and testing of intelligent control systems to increase the productivity of technological processes

Abstract

This study examines the development and testing of intelligent control systems (ISMS) aimed at improving the productivity of various technological processes. The relevance of this topic is due to the rapid development of information technology and the increasing need to optimize production cycles to achieve maximum efficiency and competitiveness of enterprises. The purpose of the work is to study the potential of using ISMS to improve technological processes and develop practical recommendations for their implementation. Research materials and methods include an analysis of existing approaches to the design of ICS, modeling of various scenarios of their functioning, as well as conducting experiments at real production facilities. In particular, methods such as neural networks, fuzzy logic, genetic algorithms and machine learning were studied. Three enterprises of various industries were selected to test the developed ISMS: a metallurgical plant, an oil refining complex and a pharmaceutical company. The results of the study showed that the introduction of ISU can significantly increase the productivity of technological processes. Thus, the metallurgical plant managed to reduce the melting time of steel by 12%, and energy consumption by 8%. At the oil refining complex, the optimization of the operation of the catalytic cracking unit led to an increase in the yield of light petroleum products by 5.6%. In a pharmaceutical company, the use of ICS to control the synthesis of active substances allowed for a 20% reduction in the number of defective products and a 15% reduction in production cycle time. The results obtained demonstrate the high efficiency of using intelligent control systems to optimize technological processes and open up broad prospects for their further application in various industries.