Soft sensor method for endpoint carbon content and temperature of BOF based on multi-cluster dynamic adaptive selection ensemble learning
Abstract: 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%.
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
Soft sensor method for endpoint carbon content and temperature of BOF based on multi-cluster dynamic adaptive selection ensemble learning ; volume:42 ; number:1 ; year:2023 ; extent:17
High temperature materials and processes ; 42, Heft 1 (2023) (gesamt 17)
- Urheber
-
Shao, Bin
Liu, Hui
Chen, Fu-gang
- DOI
-
10.1515/htmp-2022-0287
- URN
-
urn:nbn:de:101:1-2023091514110951201761
- Rechteinformation
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
14.08.2025, 10:44 MESZ
Datenpartner
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Shao, Bin
- Liu, Hui
- Chen, Fu-gang
Ähnliche Objekte (12)
Soft sensor method of multimode BOF steelmaking endpoint carbon content and temperature based on vMF-WSAE dynamic deep learning
Stability and the Growth of the Number of Closed Characteristics on Compact Convex Hypersurfaces
Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection
BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection
Non-hyperbolic P -Invariant Closed Characteristics on Partially Symmetric Compact Convex Hypersurfaces
Closed Geodesics on Positively Curved Finsler 3-Spheres
Three-point traction functions like full-face traction: facilitating rapid excision of a laterally spreading tumor
(Anti-)Stokes scattering on kinks
Hyers–Ulam stability of linear fractional differential equations with variable coefficients
The relationship of malaria between Chinese side and Myanmar’s five special regions along China–Myanmar border: a linear regression analysis
Vulnerability of China’s nearshore ecosystems under intensive mariculture development
Geometric analysis of concept vectors based on similarity values
Soft sensor method of multimode BOF steelmaking endpoint carbon content and temperature based on vMF-WSAE dynamic deep learning
Stability and the Growth of the Number of Closed Characteristics on Compact Convex Hypersurfaces
Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection
BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection
Non-hyperbolic P -Invariant Closed Characteristics on Partially Symmetric Compact Convex Hypersurfaces
Closed Geodesics on Positively Curved Finsler 3-Spheres
Three-point traction functions like full-face traction: facilitating rapid excision of a laterally spreading tumor
(Anti-)Stokes scattering on kinks
Hyers–Ulam stability of linear fractional differential equations with variable coefficients
The relationship of malaria between Chinese side and Myanmar’s five special regions along China–Myanmar border: a linear regression analysis
Vulnerability of China’s nearshore ecosystems under intensive mariculture development
Geometric analysis of concept vectors based on similarity values
Soft sensor method of multimode BOF steelmaking endpoint carbon content and temperature based on vMF-WSAE dynamic deep learning
Stability and the Growth of the Number of Closed Characteristics on Compact Convex Hypersurfaces
Endpoint carbon content and temperature prediction model in BOF steelmaking based on posterior probability and intra-cluster feature weight online dynamic feature selection
BOF steelmaking endpoint carbon content and temperature soft sensor model based on supervised weighted local structure preserving projection
Non-hyperbolic P -Invariant Closed Characteristics on Partially Symmetric Compact Convex Hypersurfaces
Closed Geodesics on Positively Curved Finsler 3-Spheres
Three-point traction functions like full-face traction: facilitating rapid excision of a laterally spreading tumor
(Anti-)Stokes scattering on kinks
Hyers–Ulam stability of linear fractional differential equations with variable coefficients
The relationship of malaria between Chinese side and Myanmar’s five special regions along China–Myanmar border: a linear regression analysis
Vulnerability of China’s nearshore ecosystems under intensive mariculture development