RUL Prediction of the Injection Lance in Copper Top-Blown Smelting Using KPCA and TSO-Optimized LSSVM

Qiang Peng; Gengen Li; Chunxi Yang; Xiufeng Zhang; Jing Na; Mou Li

doi:10.62762/TSCC.2025.978286

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Volume 2, Issue 4, ICCK Transactions on Sensing, Communication, and Control

Volume 2, Issue 4, 2025

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ICCK Transactions on Sensing, Communication, and Control, Volume 2, Issue 4, 2025: 238-249

Free to Read | Research Article | 30 November 2025

RUL Prediction of the Injection Lance in Copper Top-Blown Smelting Using KPCA and TSO-Optimized LSSVM

Qiang Peng 1

Gengen Li 2,3

Chunxi Yang 2,3 *

Xiufeng Zhang 2,3

Jing Na 2,3

Mou Li 2,3

1 Huize Smelting Branch, Yunnan Chihong Zinc and Germanium Co., Ltd., Huize 654200, China

2 Faculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming 650500, China

3 Yunnan Key Laboratory of Intelligent Control and Application, Kunming University of Science and Technology, Kunming 650500, China

* Corresponding Author: Chunxi Yang, [email protected]

DOI: 10.62762/TSCC.2025.978286

Received: 28 October 2025, Accepted: 26 November 2025, Published: 30 November 2025

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Abstract

As the core component of the copper top-blown smelting, the service life of the injection lance critically affects production stability. To monitor the operating condition of the injection lance, a data-driven model is proposed to predict the Remaining Useful Life (RUL) or service life, namely, the DKT-LSSVM model. Firstly, to reduce noise interference, the Daubechies wavelet with four vanishing moments (DB4) denoising is used to process the raw data. Then, the Kernel Principal Component Analysis (KPCA) method is utilized to extract the principal components from the denoised data, which retains at least 90% information content (18 principal components are obtained). These principal components are used as inputs to a Least Square Support Vector Machine (LSSVM) model to predict the RUL of the injection lance, and a Tuna Swarm Optimization (TSO) algorithm is proposed to optimize the hyperparameters of the LSSVM. The results show that the proposed algorithm performs well in RUL prediction of the injection lance, with RMSE=1.2274 (day), MAE=0.6623 (day) and R$^2$=0.9308. Therefore, the proposed algorithm can provide effective RUL prediction for the injection lance, reduce its operational risks, and improve the stability and reliability of the copper top-blown smelting system.

Graphical Abstract

Keywords

injection lance

least square support vector machine (LSSVM)

tuna swarm optimization (TSO) algorithm

remaining useful life (RUL)

Data Availability Statement

Data will be made available on request.

Funding

This work was supported in part by the Yunnan Major Scientific and Technological Projects, China under Grant 202302AD080005; in part by the Yunnan Fundamental Research Projects under Grant 202301AT070401.

Conflicts of Interest

Qiang Peng is an employee of Huize Smelting Branch, Yunnan Chihong Zinc and Germanium Co., Ltd., Huize 654200, China. The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

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APA Style

Peng, Q., Li, G., Yang, C., Zhang, X., Na, J., & Li, M. (2025). RUL Prediction of the Injection Lance in Copper Top-Blown Smelting Using KPCA and TSO-Optimized LSSVM. ICCK Transactions on Sensing, Communication, and Control, 2(4), 238–249. https://doi.org/10.62762/TSCC.2025.978286

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TY  - JOUR
AU  - Peng, Qiang
AU  - Li, Gengen
AU  - Yang, Chunxi
AU  - Zhang, Xiufeng
AU  - Na, Jing
AU  - Li, Mou
PY  - 2025
DA  - 2025/11/30
TI  - RUL Prediction of the Injection Lance in Copper Top-Blown Smelting Using KPCA and TSO-Optimized LSSVM
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 2
IS  - 4
SP  - 238
EP  - 249
DO  - 10.62762/TSCC.2025.978286
UR  - https://www.icck.org/article/abs/TSCC.2025.978286
KW  - injection lance
KW  - least square support vector machine (LSSVM)
KW  - tuna swarm optimization (TSO) algorithm
KW  - remaining useful life (RUL)
AB  - As the core component of the copper top-blown smelting, the service life of the injection lance critically affects production stability. To monitor the operating condition of the injection lance, a data-driven model is proposed to predict the Remaining Useful Life (RUL) or service life, namely, the DKT-LSSVM model. Firstly, to reduce noise interference, the Daubechies wavelet with four vanishing moments (DB4) denoising is used to process the raw data. Then, the Kernel Principal Component Analysis (KPCA) method is utilized to extract the principal components from the denoised data, which retains at least 90% information content (18 principal components are obtained). These principal components are used as inputs to a Least Square Support Vector Machine (LSSVM) model to predict the RUL of the injection lance, and a Tuna Swarm Optimization (TSO) algorithm is proposed to optimize the hyperparameters of the LSSVM. The results show that the proposed algorithm performs well in RUL prediction of the injection lance, with RMSE=1.2274 (day), MAE=0.6623 (day) and R$^2$=0.9308. Therefore, the proposed algorithm can provide effective RUL prediction for the injection lance, reduce its operational risks, and improve the stability and reliability of the copper top-blown smelting system.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -

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@article{Peng2025RUL,
  author = {Qiang Peng and Gengen Li and Chunxi Yang and Xiufeng Zhang and Jing Na and Mou Li},
  title = {RUL Prediction of the Injection Lance in Copper Top-Blown Smelting Using KPCA and TSO-Optimized LSSVM},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {4},
  pages = {238-249},
  doi = {10.62762/TSCC.2025.978286},
  url = {https://www.icck.org/article/abs/TSCC.2025.978286},
  abstract = {As the core component of the copper top-blown smelting, the service life of the injection lance critically affects production stability. To monitor the operating condition of the injection lance, a data-driven model is proposed to predict the Remaining Useful Life (RUL) or service life, namely, the DKT-LSSVM model. Firstly, to reduce noise interference, the Daubechies wavelet with four vanishing moments (DB4) denoising is used to process the raw data. Then, the Kernel Principal Component Analysis (KPCA) method is utilized to extract the principal components from the denoised data, which retains at least 90\% information content (18 principal components are obtained). These principal components are used as inputs to a Least Square Support Vector Machine (LSSVM) model to predict the RUL of the injection lance, and a Tuna Swarm Optimization (TSO) algorithm is proposed to optimize the hyperparameters of the LSSVM. The results show that the proposed algorithm performs well in RUL prediction of the injection lance, with RMSE=1.2274 (day), MAE=0.6623 (day) and R\$^2\$=0.9308. Therefore, the proposed algorithm can provide effective RUL prediction for the injection lance, reduce its operational risks, and improve the stability and reliability of the copper top-blown smelting system.},
  keywords = {injection lance, least square support vector machine (LSSVM), tuna swarm optimization (TSO) algorithm, remaining useful life (RUL)},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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