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Evaluation model of aluminum electrolysis cell condition based on multi-source heterogeneous data fusion

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Abstract

Industrial process data have the characteristics of heterogeneity, dimensional inconsistency and multi time scales, which increase the difficulty of condition evaluation in industrial process using multi-source data. To address these problems, a multi-source heterogeneous data fusion model is proposed for the condition evaluation of aluminum electrolysis cell. Firstly, the deep residual network (ResNet) is used to extract the superheat degree features of the fire hole video, the ResNet and wavelet packet are used to extract the cell voltage features, thereby achieving the isomorphism of heterogeneous data. An anode current features extraction method based on dynamic pivotal sequence is used to reduce the dimension of anode current data and extract features. Then, a fusion model of feature layer and data layer based on CatBoost is proposed, which comprehensively considers the material-energy balance mechanism knowledge and current efficiency to describe the dynamic coupling relationship between various data sources. The experimental evaluation results on the actual industrial aluminum electrolysis dataset show that our method improves the performance by 2.3% compared with existing multi-source data fusion method.

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Data availability

The data that support the findings of this study are available from aluminum electrolysis enterprises in Ningxia Hui Autonomous Region, but restrictions apply to the availability of these data, which were used under licence for the current study and so are not publicly available. The data are, however, available from the authors upon reasonable request and with the permission of aluminum electrolysis enterprises in Ningxia Hui Autonomous Region.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 62133016), the Basic Science Research Center Program of National Natural Science Foundation of China (Grant No.61988101), the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61725306).

Funding

National Natural Science Foundation of China, 62133016, Xiaofang Chen, 61988101, Weihua Gui, National Science Foundation for Distinguished Young Scholars of China, 61725306, Yongfang Xie.

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  1. School of Automation, Central South University, Changsha, 410083, China

    Yubo Sun, Weihua Gui, Xiaofang Chen & Yongfang Xie

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  1. Yubo Sun
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Correspondence to Xiaofang Chen.

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Sun, Y., Gui, W., Chen, X. et al. Evaluation model of aluminum electrolysis cell condition based on multi-source heterogeneous data fusion. Int. J. Mach. Learn. & Cyber. 15, 1375–1396 (2024). https://doi.org/10.1007/s13042-023-01973-9

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