A Probabilistic Model and Capturing Device for Remote Simultaneous Estimation of Spectral Emissivity and Temperature of Hot Emissive Materials

Abstract

Estimating the temperature of hot emissive samples (e.g. liquid slag) in the context of harsh industrial environments such as steelmaking plants is a crucial yet challenging task, which is typically addressed by means of methods that require physical contact. Current remote methods require information on the emissivity of the sample. However, the spectral emissivity is dependent on the sample composition and temperature itself, and it is hardly measurable unless under controlled laboratory procedures. In this work, we present a portable device and associated probabilistic model that can simultaneously produce quasi real-time estimates for temperature and spectral emissivity of hot samples in the [0.2, $12.0 \boldsymbol {\mu }m$ ] range at distances of up to $20 \boldsymbol {m}$ . The model is robust against variable atmospheric conditions, and the device is presented together with a quick calibration procedure that allows for in field deployment in rough industrial environments, thus enabling in line measurements. We validate the temperature and emissivity estimates by our device against laboratory equipment under controlled conditions in the [550, $850^{\circ } \boldsymbol {C}$ ] temperature range for two solid samples with well characterized spectral emissivity’s: alumina ( $\boldsymbol {\alpha -Al_{2}O_{3}}$ ) and hexagonal boron nitride ( $\boldsymbol {h-BN}$ ). The analysis of the results yields Root Mean Squared Errors of $32.3^{\circ } \boldsymbol {C}$ and $5.7^{\circ } \boldsymbol {C}$ respectively, and well correlated spectral emissivity’s.

Keywords

• Probabilistic computing
• radiometry
• spectral analysis
• spectral emissivity
• spectroscopy
• steel industry