Soft sensor method for endpoint carbon content and temperature of BOF based on multi-cluster dynamic adaptive selection ensemble learning

• Published in: High temperature materials and processes Vol. 42; no. 1; pp. id. 100356 - 88
• Main Authors: Shao, Bin, Liu, Hui, Chen, Fu-gang
• Format: Journal Article
• Language: English
• Published: Berlin De Gruyter 01.01.2023; Walter de Gruyter GmbH
• Subjects: Adaptive control; Algorithms; Basic converters; BOF; Carbon; Carbon content; Clustering; dynamic adaptive selection; Emissions control; endpoint carbon content and temperature prediction; Ensemble learning; Machine learning; Oxygen steel making; Performance degradation; Queries; soft sensor; Steel converters; Teaching methods
• ISSN: 2191-0324; 0334-6455; 2191-0324
• DOI: 10.1515/htmp-2022-0287

The accurate control of the endpoint in converter steelmaking is of great significance and value for energy saving, emission reduction, and steel quality improvement. The key to endpoint control lies in accurately predicting the carbon content and temperature. Converter steelmaking is a dynamic process with a large fluctuation of samples, and traditional ensemble learning methods ignore the differences among the query samples and use all the sub-models to predict. The different performances of each sub-model lead to the performance degradation of ensemble learning. To address this issue, we propose a soft sensor method based on multi-cluster dynamic adaptive selection (MC-DAS) ensemble learning for converter steelmaking endpoint carbon content and temperature prediction. First, to ensure the diversity of the ensemble learning base model, we propose a clustering algorithm with different data partition characteristics to construct a pool of diverse base models. Second, a model adaptive selection strategy is proposed, which involves constructing diverse similarity regions for individual query samples and assessing the model’s performance in these regions to identify the most suitable model and weight combination for each respective query sample. Compared with the traditional ensemble learning method, the simulation results of actual converter steelmaking process data show that the prediction accuracy of carbon content within ±0.02% error range reaches 92.8%, and temperature within ±10°C error range reaches 91.6%.