Stacked ensemble learning model-based prediction and optimization of the grade of titanium dioxide in high titanium slag

ZHOU Kaimin; CAI Jinqiu; WANG Kaixuan; HOU Yanqing; HOU Jiao; WANG Jianguo

doi:10.7513/j.issn.1004-7638.2025.05.012

Volume 46 Issue 5

Oct. 2025

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ZHOU Kaimin, CAI Jinqiu, WANG Kaixuan, HOU Yanqing, HOU Jiao, WANG Jianguo. Stacked ensemble learning model-based prediction and optimization of the grade of titanium dioxide in high titanium slag[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 111-122. doi: 10.7513/j.issn.1004-7638.2025.05.012

Citation:

ZHOU Kaimin, CAI Jinqiu, WANG Kaixuan, HOU Yanqing, HOU Jiao, WANG Jianguo. Stacked ensemble learning model-based prediction and optimization of the grade of titanium dioxide in high titanium slag[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 111-122. doi: 10.7513/j.issn.1004-7638.2025.05.012

Citation:

ZHOU Kaimin, CAI Jinqiu, WANG Kaixuan, HOU Yanqing, HOU Jiao, WANG Jianguo. Stacked ensemble learning model-based prediction and optimization of the grade of titanium dioxide in high titanium slag[J]. IRON STEEL VANADIUM TITANIUM, 2025, 46(5): 111-122. doi: 10.7513/j.issn.1004-7638.2025.05.012

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Stacked ensemble learning model-based prediction and optimization of the grade of titanium dioxide in high titanium slag

doi: 10.7513/j.issn.1004-7638.2025.05.012

1.
Yunnan Chihong Zinc and Germanium Co., Ltd., Qujing 655011, Yunnan, China
2.
School of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, Yunnan, China
3.
Yunnan Diqing Nonferrous Metals Co., Ltd., Shangri-La 674408, Yunnan, China

More Information

Received Date: 2025-07-25
Accepted Date: 2025-09-01
Rev Recd Date: 2025-09-01
Publish Date: 2025-10-30

Abstract

Abstract

Titanium dioxide (TiO₂), the primary component of high-titanium slag, is widely used in various industrial fields such as coatings, plastics, papermaking, and cosmetics. This paper proposed a TiO₂ grade prediction method based on a stacked model that combines random forest (RF), gradient boosting machine (GBM), and support vector regression (SVR) to optimize the prediction accuracy of TiO₂ grade in high-titanium slag through ensemble learning. The dataset was obtained from raw production data in a metallurgical plant. After data processing and feature derivation, dimensionality reduction techniques were applied to reduce the number of feature variables from 33 to 15, thereby identifying the most informative variables. Experimental results demonstrate that the stacked regression model achieves excellent performance on the validation and test sets, with an R² value of 0.9249, MAPE values of 0.29% and 0.30%, and MSE values of 0.177 and 0.182, respectively—outperforming individual models. SHAP value analysis further revealed that feature variables such as the TiO₂/FeO mass ratio and C content positively influence the TiO₂ grade within certain ranges. Moreover, by integrating the stacked model with Monte Carlo simulations, the optimal ranges of key feature variables were determined, such as the TiO₂/FeO ratio (1.70–2.12) and the TiO₂/C ratio (0.50–0.58). This approach avoids the trial-and-error process and energy waste associated with traditional empirical methods, thereby contributing to energy conservation and emission reduction while enhancing the production efficiency and product quality of titanium slag.
- TiO₂ grade prediction,
- ensemble learning,
- stacked model,
- metallurgical process optimization,
- feature importance analysis

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References(44)

References

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