Neodymium oxide concentration state recognition model in neodymium molten salt electrolysis process based on flame image features

• Published in: Measurement : journal of the International Measurement Confederation Vol. 239; p. 115495
• Main Authors: Zhang, Zhen, Xu, Meijuan, Ming, Keke, Liu, Feifei, He, Xinfeng, Zhang, Xiang
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.01.2025
• Subjects: Deep learning; Flame characteristics; Image segmentation; Neodymium oxide concentration; Pattern recognition; Deep learning; Image segmentation; Neodymium oxide concentration; Flame characteristics; Pattern recognition
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.115495

[Display omitted] •Established a Nd2O3 concentration state recognition model based on flame features.•Proposed a skip connection enhanced (SCE) module.•Proposed a lightweight flame segmentation model FEC-Net.•Establishment of the Nd Electrolysis Cell Flame Segmentation Dataset (NECFS). Accurate recognition of the concentration state of neodymium-oxide (Nd2O3) in the neodymium (Nd) molten salt electrolysis process provides crucial feedback information for online adjustment of process parameters during Nd metal batch production. This paper proposes a Nd2O3 concentration state recognition model based on flame image features. The model employs the lightweight flame segmentation algorithm (FEC-Net) to accurately segment the flame regions in the collected Nd electrolysis cell flame images. Then, using image processing techniques, geometric and color features of the flame regions are extracted. Finally, a Support Vector Machine (SVM) is used to establish a nonlinear mapping model between the flame features and Nd2O3 concentration states. This model classifies the collected flame image dataset to verify the Nd2O3 concentration state during a certain period of Nd molten salt electrolysis. Experiments demonstrate that the recognition accuracy of SVM reaches 96.09%, meeting the monitoring requirements of the Nd molten salt electrolysis process.