EAF Best Practice Analysis

The EAF Best Practice Analysis is a data-based service designed to optimize electric arc furnace (EAF) operations. In the steel industry, challenges such as minimizing scrap costs, conversion costs, and tap-to-tap time are critical for maintaining competitiveness. This service leverages a self-developed big data filtering and sorting engine to assist EAF operators in achieving their specific operational goals. By analyzing actual heats, the service identifies the best practices tailored to individual needs, providing significant added value to steel producers.

The EAF Best Practice Analysis addresses key needs of steel producers by determining the optimal operation strategy, focusing on scrap costs, conversion costs, and tap-to-tap time. This service is particularly vital in the face of fluctuating market conditions. It standardizes the electric arc furnace production process, developing consistent scrap basket recipes. By visualizing the impact of different scrap qualities, it enhances the understanding of optimal electric arc furnace operations.

Without leveraging big production data, electric arc furnace operations can suffer from suboptimal performance. Conventional methods, such as analyzing exported data in Excel, often lead to a loss of focus and omit critical time series data. The EAF Best Practice Analysis uses real heats, rather than estimated data, allowing for quick adaptation to varying targets such as fluctuating steel prices and CO₂ emissions. It predicts conversion costs and calculates the optimum scrap mix costs, ensuring a transparent and understandable analysis process.

Benefits of the EAF Best Practice Analysis

• Enhances tap-to-tap time efficiency, boosting overall productivity
• Provides a clear visualization of scrap quality impacts on operations
• Facilitates quick adaptation to market changes with real-time data analysis
• Ensures transparency and comprehensibility in every analysis step
• Utilizes reference heats for rapid response to operational targets
• Predicts conversion costs and optimizes scrap mix for cost efficiency