Predictive Modelling of Nitrogen Content in Molten Metal During BOF Steelmaking Processes via Python-Based Machine Learning: A Benchmarking of Statistical Techniques

This study benchmarks eight Python-based machine learning models for predicting nitrogen content across four sequential stages of BOF steelmaking. A dataset of 291 metallic samples from 76 heats was employed, covering pig iron desulfurization (PHASE #1), crude steel before BOF tapping (PHASE #2), and secondary metallurgy start (PHASE #3) and completion (PHASE #4). Linear regression, polynomial regression, ridge regression, decision tree, random forest, feedforward neural networks (FNNs), Gaussian Process Regression (GPR), and Support Vector Regression (SVR) were implemented in Python 3 with Z-score normalization and an 80/20 train–test split, and evaluated via MAE, MSE, MAPE, and R2. Ridge regression achieved the highest accuracy in PHASE #1 (84.59%) and PHASE #4 (84.04%); FNNs excelled in PHASE #2 (78.27%) with consistent cross-phase performance; linear regression was optimal for PHASE #3 (79.06%). The advanced kernel-based methods demonstrated competitive performance, with GPR achieving 84.73% in PHASE #1 and SVR attaining 77.10% in PHASE #3 and 83.40% in PHASE #4, confirming their suitability for limited industrial datasets with a nonlinear structure. A hybrid strategy remains recommended: ridge regression for PHASES #1 and #4, FNNs for PHASES #2 and #4, and linear regression for PHASE #3, with SVR as a robust alternative in phases with moderate nonlinearity.