Open Access
ARTICLE
A Computational Multi-Output Soft Sensing Framework for Sinter Quality Prediction Using Feature Selection and Hierarchical SVR Optimization
Zhenhua Yang1,2, Yifan Li1,2, Aimin Yang1,2,*, Jie Li2,3, Tao Xue1,2
1 College of Science, North China University of Science and Technology, Tangshan, China
2 Tangshan Key Laboratory of Engineering Computation, Tangshan, China
3 College of Metallurgy and Energy, North China University of Science and Technology, Tangshan, China
* Corresponding Author: Aimin Yang. Email: 
Computer Modeling in Engineering & Sciences https://doi.org/10.32604/cmes.2026.081754
Received 08 March 2026; Accepted 27 April 2026; Published online 14 May 2026
Abstract
Sinter quality prediction in iron ore sintering is a challenging computational modeling problem because of highly nonlinear process behavior, strong cross-variable interactions, and disturbances caused by changing operating conditions. This study develops a data-driven multi-index soft-sensing framework for sinter quality prediction by combining feature selection and hierarchical model optimization. An improved binary Greylag Goose Optimization algorithm is first employed to identify a compact subset of informative variables, reducing redundancy and multicollinearity in the original process data. A hierarchical two-stage Greylag Goose Optimization strategy is then designed to optimize the hyperparameters of a support vector regression model through coarse-to-fine search, balancing global exploration and local refinement in the parameter space. The proposed framework is validated on three key sinter quality indices under consistent data partitioning and equal optimization budgets. Experimental results show that the method achieves coefficients of determination of 0.975, 0.985, and 0.986 for yield, drum index, and RDI
+3.15, respectively, indicating strong predictive capability and robust generalization. Comparative experiments demonstrate that the proposed framework outperforms several representative baseline methods in terms of prediction accuracy and fitting performance. In addition, ablation analysis confirms the contribution of the hierarchical optimization mechanism to the overall model performance. The proposed framework offers an effective computational approach for multi-index quality modeling, online prediction, and intelligent decision support in complex industrial systems.
Keywords
Data-driven modeling; multi-index quality prediction; support vector regression; feature selection; Greylag Goose Optimization; hierarchical optimization
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