Monitoring Sintering Burn-Through Point Using Infrared Thermography

Sintering is a complex industrial process that applies heat to fine particles of iron ore and other materials to produce sinter, a solidified porous material used in blast furnaces. The sintering process needs to be carefully adjusted, so that the combustion zone reaches the bottom of the material just before the discharge end. This is known as the burn-through point. Many different parameters need to be finely tuned, including the speed and the quantities of the materials mixed. However, in order to achieve good results, sintering control requires precise feedback to adjust these parameters. This work presents a sensor to monitor the sintering burn-through point based on infrared thermography. The proposed procedure is based on the acquisition of infrared images at the end of the sintering process. At this position, infrared images contain the cross-section temperatures of the mixture. The objective of this work is to process this information to extract relevant features about the sintering process. The proposed procedure is based on four steps: key frame detection, region of interest detection, segmentation and feature extraction. The results indicate that the proposed procedure is very robust and reliable, providing features that can be used effectively to control the sintering process.

[1]  R. German Sintering theory and practice , 1996 .

[2]  Meng Joo Er,et al.  Fuzzy neural networks-based quality prediction system for sintering process , 2000, IEEE Trans. Fuzzy Syst..

[3]  Xavier Maldague,et al.  Infrared image processing and data analysis , 2004 .

[4]  P. Childs,et al.  Review of temperature measurement , 2000 .

[5]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[6]  Eero P. Simoncelli,et al.  Differentiation of discrete multidimensional signals , 2004, IEEE Transactions on Image Processing.

[7]  Yu-tian Wang,et al.  The research of control for sintering burn-through point based on finite-state machine , 2011, 2011 International Conference on Electric Information and Control Engineering.

[8]  Daniel F. García,et al.  Temperature Measurement of Molten Pig Iron With Slag Characterization and Detection Using Infrared Computer Vision , 2012, IEEE Transactions on Instrumentation and Measurement.

[9]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[10]  Suk‐Joong L. Kang,et al.  Sintering: Densification, Grain Growth and Microstructure , 2005 .

[11]  Daniel F. García,et al.  Real-time line scan extraction from infrared images using the wedge method in industrial environments , 2010, J. Electronic Imaging.

[12]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[13]  P. Venegas,et al.  Automatic detection of impact damage in carbon fiber composites using active thermography , 2013 .

[14]  M.-J. Posada Rodríguez,et al.  Sintering process burn-through point modelization , 2009 .

[15]  S. A. R. Abu-Bakar,et al.  Defect detection in thermal image for nondestructive evaluation of petrochemical equipments , 2009 .

[16]  Jun Liu,et al.  Data-driven prediction of sintering burn-through point based on novel genetic programming , 2010 .

[17]  Shigeo Abe DrEng Pattern Classification , 2001, Springer London.

[18]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[19]  Bidyut Baran Chaudhuri,et al.  A survey of Hough Transform , 2015, Pattern Recognit..

[20]  Sang Jeong Lee,et al.  Event-based modeling and control for the burnthrough point in sintering processes , 1999, IEEE Trans. Control. Syst. Technol..

[21]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[22]  Chunming Li,et al.  Minimization of Region-Scalable Fitting Energy for Image Segmentation , 2008, IEEE Transactions on Image Processing.

[23]  Xavier Maldague,et al.  Theory and Practice of Infrared Technology for Nondestructive Testing , 2001 .

[24]  Kishalay Mitra,et al.  Optimisation of suction pressure for iron ore sintering by genetic algorithm , 2004 .

[25]  Denis Friboulet,et al.  Creaseg: A free software for the evaluation of image segmentation algorithms based on level-set , 2010, 2010 IEEE International Conference on Image Processing.

[26]  Toshiyuki Nakamiya,et al.  Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring , 2012, Sensors.

[27]  Mohammed A. Omar,et al.  Dynamic-template processing for passive thermograms: Applied to automotive stamping split detection , 2008 .

[28]  P. Venegas,et al.  Non-destructive inspection of drilled holes in reinforced honeycomb sandwich panels using active thermography , 2012 .

[29]  Jun-hong Zhang,et al.  Multi-Objective Optimization and Analysis Model of Sintering Process Based on BP Neural Network , 2007 .