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Optical Noninvasive Temperature Measurement of Molten Melts in Metallurgical Process: A Review

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Abstract

High-temperature melts, including molten metals and molten melts, are common substances in the pyrometallurgical process. Temperature is one of the most concerned and important physical properties of melts. Maintaining a melt’s molten state means huge energy consumption. Moreover, the temporary storage, transfer, and subsequent processing of them are usually accompanied by considerable risks. Thus, real-time temperature measurement of the melt is one of the core requirements. Due to the high corrosiveness of molten phases, invasive temperature measurement is no longer applicable or even could not deploy in high-temperature melts. Therefore, optical temperature measurement technology has been brought into prime focus. This paper provides a review of the academic improvement involved and commercial technology employed in each optical temperature measurement device. Recent progress and trends are investigated in both radiation thermometers and computer vision-based pyrometers. The measurement principle and characteristics of each device are discussed in detail. The optimization of accuracy is emphasized based on radiation thermometers, while the reasons for a few computer vision-based pyrometers are mentioned. In addition, a new temperature diagnosis technology that combines computer vision and artificial intelligence has received widespread attention and applications in recent years. Future developments are briefly introduced to provide some insight into which direction the development and application of temperature measurement technologies are likely to develop.

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Project supported by Jiangxi Ionic Rare Earth Resources Green Development and High-value Utilization of National Key Laboratory Cultivation Program (20194AFD44003) and Jiangxi Province Key Innovation Research and Development Platform Plan of China (20181BCD40009).

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Wu, X., Li, J., Liu, F. et al. Optical Noninvasive Temperature Measurement of Molten Melts in Metallurgical Process: A Review. MAPAN 37, 793–809 (2022). https://doi.org/10.1007/s12647-022-00583-0

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