Ferroalloys Consumption

Ferroalloys consumption optimization in industrial processes, particularly steelmaking, is a significant area of research focusing on minimizing material waste and improving efficiency. Current efforts leverage machine learning techniques, including clustering algorithms (like k-means) combined with regression models (e.g., linear regression, decision trees) to predict optimal ferroalloy additions based on process parameters and historical data. These models aim to provide interpretable recommendations for process control, leading to more precise adjustments and reduced consumption. The successful application of these methods promises substantial economic benefits and improved environmental sustainability within the metallurgical industry.