BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection

Abstract

Endpoint control stands as a pivotal determinant of steel quality. However, the data derived from the BOF steelmaking process are characterized by high dimension, with intricate nonlinear relationships between variables and diverse working conditions. Traditional dimension reduction does not fully use non-local structural information within manifold shapes. To address these challenges, the article introduces a novel approach termed supervised weighting-based local structure preserving projection. This method dynamically includes label information using sparse representation and constructs weighted submanifolds to mitigate the influence of irrelevant labels. Subsequently, trend match is employed to establish the same distribution datasets for the submanifold. The global and local initial neighborhood maps are then constructed, extracting non-local relations from the submanifold by analyzing manifold curvature. This process eliminates interference from non-nearest-neighbor points on the manifold while preserving the local geometric structure, facilitating adaptive neighborhood parameter change. The proposed method enhances the adaptability of the model to changing working conditions and improves overall performance. The carbon content prediction maintains a controlled error range of within ±0.02%, achieving an accuracy rate of 82.50%. The temperature prediction maintains a controlled error range of within ±10°C, achieving an accuracy rate of 79.00%.

Keywords

• bof steelmaking endpoint
• soft sensor
• just-in-time
• dimension reduction