<b>Predictive Modeling and Inverse FEM Simulation for Enhancing Steelmaking Operations</b>

<p dir="ltr">This thesis implements state-of-the-art methods and introduces novel approaches in data processing, machine learning, and Finite Element Method (FEM)-based simulations to enhance the operation of a specific blast furnace in Indiana, USA. We direct our efforts into incorporating myriads of multi-sourced, incongruent, and semi structured data to build predictive tools that offer insights into furnace conditions and subsequent decision-making process. Two primary models are developed—one currently in beta testing and another transitioning from alpha to beta phase. The first model focuses on predicting hot metal silicon content, a key indicator of furnace performance. By employing a generalized data processing scheme and a robust XGBoost-based modeling pipeline, the model achieves <b>91% accuracy</b> over <b>200 real test data</b> predictions using only the previous operating conditions. In turn, the model can provide real-time guidance for operational adjustments that reduce risk and improve performance. The second model provides an estimate of hearth erosion, a critical concern due to the severe thermal and mechanical stresses in that zone. The efficient and minimal FEM-based model uses conductivity driven formulations, vectorized SIMD and parallelization operations and residual gradients for optimization to rapidly estimate erosion profiles without mesh reconstruction or proprietary CFD tools. It achieves <b>5–25°C</b> <b>temperature</b> accuracy with <b>convergence in 3-5 minutes</b> on a 6 core CPU. Validated with industrial data, it supports long-term erosion tracking and safety planning despite steady-state assumptions. Together, these contributions offer practical, high-impact tools for improving furnace safety, reducing downtime, extending campaign life, maintaining productivity, reducing associated costs and facilitating better and on-time decision making.</p>