Waste management for production of steel electric welding pipes using Data Mining technologies and MES systems

Abstract

The article is devoted to the study of environmental problems in the production of steel pipes and the rational use of waste. The relevance of the study lies in the high resource intensity and waste of pipe metallurgy, the classification of steel production and their subsequent redistribution as the first category of environmental hazard and is assigned to the areas of application of the best available technologies. The aim of the article is to analyze the resource intensity and waste production of steel pipes, to study the existing waste management system and its rationality at Russian enterprises. As the main research methods, the IDEF0 modeling methodology was used to visualize the waste management system and to structurize the model for the production of welded pipes taking into account emissions of pollutants, as well as Data Mining technology in the form of a classification and regression tree for studying trends and local patterns in waste management. The tendency of increasing resource intensity at the enterprise of the pipe industry with relative financial instability is revealed. A visualization of the waste management system in the production of steel pipes is proposed, which makes it possible to distinguish the main functional blocks of the incoming and outgoing parts of the waste balance. A model of the processes of production of welded pipes is structured taking into account emissions of polluting substances into the atmosphere and cleaning methods according to the reference book on the best available technologies ITR 27-2017. Based on the constructed decision tree, knowledge was obtained on the trends in the treatment of steel pipe production waste. In order to reduce the level of rejection in the production of steel pipes and minimize the formation of scrap, a project has been proposed on the automation of technical quality control and the implementation of the MES system. The research was carried out within the framework of the grant of the President of the Russian Federation for state support of leading scientific schools of the Russian Federation, project number NSh-2600.2020.6.