Multi-modal deep feature fusion for superheat identification in aluminum electrolysis

Jing Zeng; Xiaofang Chen; Yongfang Xie; Zhong Zou

doi:10.1117/12.3024737

3 April 2024 Multi-modal deep feature fusion for superheat identification in aluminum electrolysis

Jing Zeng, Xiaofang Chen, Yongfang Xie, Zhong Zou

Proceedings Volume 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023); 130780J (2024) https://doi.org/10.1117/12.3024737
Event: Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 2023, Wuhan, China

Abstract

Superheat is one of the key indicators for reflecting production efficiency in aluminum electrolysis industry. To address the problem of insufficient performance in superheat identification with single modal data, a multi-modal deep feature fusion (MMDFF) model is proposed for superheat identification. By considering the global and local features synchronously, a cross-modal information interaction block is developed to improve the accuracy of superheat identification, which enriches the information expression of each mode. Finally, the effectiveness of the proposed superheat identification is demonstrated by the comparison experiments conducted on multi-source datasets of aluminum electrolysis industrial process. Meanwhile, the ablation experiments are used to verify the superiority of the cross-modal information interaction block.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jing Zeng, Xiaofang Chen, Yongfang Xie, and Zhong Zou "Multi-modal deep feature fusion for superheat identification in aluminum electrolysis", Proc. SPIE 13078, Second International Conference on Informatics, Networking, and Computing (ICINC 2023), 130780J (3 April 2024); https://doi.org/10.1117/12.3024737

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KEYWORDS

Data modeling

Aluminum

Video

Data processing

Education and training

Feature fusion

Feature extraction

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