Volume IV-1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 53-60, 2018
https://doi.org/10.5194/isprs-annals-IV-1-53-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 53-60, 2018
https://doi.org/10.5194/isprs-annals-IV-1-53-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

INFRARED MEASUREMENTS AND ESTIMATION OF TEMPERATURE IN THE RESTRICTIVE SCOPE OF AN INDUSTRIAL CEMENT PLANT

R. Gabriel1, S. Keller2, J. Matthes3, P. Waibel3, H. B. Keller3, and S. Hinz2 R. Gabriel et al.
  • 1ci-tec GmbH, 76137 Karlsruhe, Germany
  • 2IPF, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
  • 3IAI, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

Keywords: Infrared measurement, Estimation, Reflectance, Temperature distribution, Clinker cooler, Cement plant, Industrial application

Abstract. In this paper, we describe and evaluate the process of estimating reflectance corrected temperatures based on infrared measurements in the scope of an industrial cement production plant. We overview the underlying cement production phases, as well as the resulting challenges for infrared-based monitoring in such an industrial environment. Our studies are focused particularly on the use of infrared sensors in the clinker cooling process. Using a highly specialized infrared camera (10.6 μm), a dataset is obtained capturing the radiation emissions of cement clinker during the clinker cooling process at a cement plant. We briefly turn on the necessity of image preprocessing and then focus on calculating reflectance corrected thermal images for temperature estimation without the use of reference markers or additional instrumentation. This study represents the first usage of infrared camera-based measurements in the clinker cooling process. The main contributions, a recorded dataset and two proposed estimation models including a linear model and a machine learning model with their respective temperature estimations, will provide the basis for the extraction of further process characteristics. Therefore, our contributions will enable scientists as well as process operators to gain new insights about the cement clinker cooling process and to optimize the cement cooling and production process automatically.