Machine learning-assited optical thermometer for continuous temperature analysis inside molten metal

Abstract

Abstract This paper demonstrates a robust optical fiber thermometer (OFT) for temperature measurement under extreme environments. To date, the development of sensors for continuous temperature measurement in environments with temperatures over 1000 ᵒC, severe electromagnetic interferences, and strong oxidizing agents has been very challenging. The proposed nano-OFT system consists of a ceramic tube, a nanorod coated sapphire fiber, and a near-infrared (NIR) spectrum analyzer for continuous measurement of molten steel temperature in furnaces. The nanorod layer functions as an effective cladding material for the sapphire fiber to sustain a reliable transmission of NIR thermal emissions. The thermal radiation from the ceramic tube s tip was coupled out of the nano-OFT probe via the sapphire fiber and measured using the NIR spectrometer. The NIR emissions were analyzed using a convolution neural network to determine the probe temperature. Our results show that the nano-OFT probe can measure furnace temperature in the temperature range from 1,000 – 1,650\u2009°C, with the error percentage as low as 0.5%. The nano-OFT system can be employed by the steel industry to monitor steel temperature continuously, and thus enhance steel production efficiency and reduce energy consumption.