Hybrid Model Integrating Locally Linear Embedding and LightGBM for Predicting Sulfur Content in Electroslag Remelting

Abstract

This study proposes a model for predicting the sulfur content in the electroslag remelting (ESR) process, by integrating locally linear embedding (LLE) and the LightGBM algorithm. The LLE dimensionality reduction method is employed to preprocess the factors influencing the sulfur content, preserving the local relationships between samples while reducing the data dimensionality. Subsequently, five models are established, including LLE‐LightGBM, LLE‐RR, LLE‐RF, LLE‐XGBoost, and LLE‐CatBoost. Bayesian optimization is used to tune the hyperparameters of these models, which are then trained and validated using production data. The results demonstrate that the integration of LLE and Bayesian optimization improved the performance of all five models. The incorporation of LLE enhanced the models’ understanding and predictive capabilities of the data, thereby improving their performance and robustness. Among the five models, the LLE‐LightGBM model exhibits the best performance, with the following metrics: MSE = 0, = 0.9894, MAE = 0, MEDAE = 0, EVS = 0.9895, and ME = 1.8 × 10−5. Therefore, the LLE‐LightGBM model can accurately predict the sulfur content in the ESR process, providing valuable guidance for end‐point control and decision‐making, and offering a new approach toward the intelligent automation of the ESR process.