The silicon content of molten iron serves as a crucial indicator of temperature trends within a blast furnace. However, the wide variation in silicon content and the time delay in offline analysis pose challenges for operators in assessing the furnace's thermal operating conditions. This project focuses on developing predictive models to anticipate silicon content in hot metal, leveraging real-time data and advanced analytics. By accurately forecasting silicon content, operators can gain valuable insights into furnace temperature trends, enabling proactive adjustments to optimize furnace performance and ensure efficient operation. This initiative aims to enhance operational efficiency and productivity in blast furnace operations.
