Nitrogen Prediction

Project Analytical modelling of nitrogen content prediction in pig iron and molten steel during steelmaking process aims to develop and apply statistical and analytical models to accurately predict nitrogen levels in molten metal at each critical stage of the steelmaking process, including desulfurized pig iron, crude steel from the Basic Oxygen Furnace (BOF), and steel at both the beginning and end of secondary steelmaking. These objectives are specific, attainable, measurable, and relevant to the project's scope.

This project will analyze non-stationary, time-varying real-world data from a steel plant. After rigorous data cleaning and preprocessing, key factors influencing nitrogen content in molten pig iron and steel will be identified through correlation analysis. To pinpoint the most significant factors, several methods of statistical distribution will be applied, and these factors will undergo further scrutiny using kinetic and thermodynamic principles. Building upon this foundation, advanced statistical techniques such as least squares regression, cointegration analysis, and econometric modeling will be employed to construct a preliminary predictive model for nitrogen content. Cloud-based machine learning will then be utilized to refine the model and enhance its predictive capabilities.

This innovative project seeks to overcome the challenge of missing or delayed nitrogen content measurements in pig iron and steel. By employing advanced statistical modeling, we aim to significantly enhance process control, optimize resource efficiency, reduce carbon emissions and energy consumption, and ultimately improve overall product quality within the Basic Oxygen Furnace (BOF). Project details is available here.