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. 2022 Sep 27;22(19):7333.
doi: 10.3390/s22197333.

Data Analysis and Modelling of Billets Features in Steel Industry

Silvia Maria Zanoli  1 , Crescenzo Pepe  1 , Elena Moscoloni  2 , Giacomo Astolfi  2
Affiliations

Data Analysis and Modelling of Billets Features in Steel Industry

Silvia Maria Zanoli et al. Sensors (Basel). .

Abstract

This study proposes a data analysis and modelization method for the rolling mill process of billets in steel plants. By exploiting rolling mill signals and advanced data processing algorithms, a reliable billet tracking system is designed, which tracks each workpiece from the furnace entrance to the rolling mill stands' exit area. Based on the stored information, two problems are addressed: the data analysis of the temperature sensors (a thermal imaging camera and pyrometers) and the current that is related to the rolling mill stands' absorption, and subsequently, a mathematical modelization of the billets' temperature along their path in the rolling mill is produced. The data analysis suggested that we should perform hardware modifications: the thermal imaging camera was repositioned to avoid the effect of scale formation on the temperature measurements. The modelization phase provided the basis for future control and/or diagnosis applications that will exploit a temperature decay model.

Keywords: billet; data analysis; modelization; reheating furnace; rolling mill stands; steel industry; tracking system.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Example of a billet.
Figure 2
Figure 2
Schematic representation of the reheating furnace.
Figure 3
Figure 3
Original configuration of the plant.
Figure 4
Figure 4
Modified configuration of the plant.
Figure 5
Figure 5
Designed configuration for data acquisition and storage.
Figure 6
Figure 6
Billet temperature sensors measurements (furnace output/rolling mill).
Figure 7
Figure 7
Rolling mill stands’ absorption measurements.
Figure 8
Figure 8
Original configuration of the plant (including details on the rolling mill phase).
Figure 9
Figure 9
Scatter plot comparing the first rolling mill pyrometer and the absorption in stand 9.
Figure 10
Figure 10
Scatter plot comparing the second rolling mill pyrometer and the absorption in stand 9.
Figure 11
Figure 11
Scatter plot comparing the thermal imaging camera and the second rolling mill pyrometer.
Figure 12
Figure 12
Modelization: 3D model of a billet, 3D model mesh, and color temperature scaling (K).
Figure 13
Figure 13
Modelization: phase 1 (movement between the thermal imaging camera and the descaler).
Figure 14
Figure 14
Modelization: phase 2 (descaler processing).
Figure 15
Figure 15
Modelization: phase 3 (movement between the descaler and stand 1).
Figure 16
Figure 16
Modelization: phase 4 (processing between rolling mill stands 1–2).
Figure 17
Figure 17
Modelization: phase 5 (movement between stand 2 and stand 3).
Figure 18
Figure 18
Modelization: phase 6 (processing between stand 3 and stand 9).
See this image and copyright information in PMC

References

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    1. Zanoli S.M., Orlietti L., Cocchioni F., Astolfi G., Pepe C. Optimization of the clinker production phase in a cement plant. In: Gonçalves J.A., Braz-César M., Coelho J.P., editors. CONTROLO 2020. Lecture Notes in Electrical Engineering. Volume 695. Springer; Cham, Switzerland: 2021. - DOI
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