Metals

• Analyze input and output data of the physical and mathematical model of the rolling mill, process parameters, and data
• Create the architecture of a digital twin of the production system
• Adapt and implement the interface to the system
• Debug and adapt the physical and mathematical model of the pipe rolling mill
• Visualize recommendations for adjusting the technological operating mode of the pipe rolling mill on the platform
• Perform 3D visualization of the hot finishing line
• Build hybrid models and analyze the results of the hybrid models by analyzing scenarios of the hybrid model's operation
• Test the platform with real data
• Develop a user manual for working on the platform

• Development of diagnostic models for equipment operation to reduce downtime in the rolling mill
• Development of optimization models to minimize the defect rate in the products
• Recommendation of control parameters

• 10-20% reduction in unscheduled equipment downtime
• Early recognition of 54% of all unscheduled downtime events
• 1-5% reduction in the proportion of defective products
• Mill productivity increase by 0.5-2%

• Optimization of the gas flow control system
• Taking into account interdependencies between control loops
• Incorporation of external system parameters

• The system generates setpoints for regulators to efficiently utilize the furnace
• Gas consumption reduction in the zones through optimal control

• Preparation of samples of modeled slags
• Preparation of a cylindrical refractory sample for conducting dynamic investigations of slag resistance
• Refinement of the methodology for determining carbon solubility in industrial slags
• Development of a regression model predicting metal carburization
• Development of a regression model predicting slag carburization
• Development of a regression model predicting refractory wear
• Experimental confirmation of the calculated hypothesis
• Conducting two experiments using slags resulting in the best and worst metal carburization

• Utilization of a unique combination of physical testing methods and digital modeling
• A platform solution in the form of a mathematical model recommending the optimal slag for a specific type of refractory
• Unique self-developed equipment for refining and validating the refractory wear model

• Forecasting the resource life of refractory products from different suppliers using model/real slags
• Recommendations for the chemical composition of slag to enhance refractory durability while preserving the technological properties of the slag

Effects

Increase in refractory lining durability by at least 5%, resulting in savings of up to $1 million per year

• Analysis of initial data and formulation of modeling tasks. Creation of a map of required calculation modes. Collection and analysis of initial data
• Development of a mathematical model for the spatial distribution of alloying additives in the filled volume of the ladle and vacuum degasser
• Creation of a calculation model
• Simulation of various technological modes according to the developed map of modes
• Processing, analysis, and summarization of modeling results. Systematization of calculation data

• Detailed hydrodynamics model of mixing and refractory wear intensity
• Creation of a detailed dynamic model of production processes
• Optimal parameters based on a combination of digital models and real operational data

• Reduction of refractory wear by up to 10%. Average savings of up to $1 million per year
• Doubling the steel mixing speed with ferroalloys
• Increase in productivity by 0.5%
• Increase in production output by $500-600 thousands per year per one vacuum degasser