Flame Image Processing-Based Intelligent Networked Control System of Roller Kiln

Abstract:

Flame image processing-based intelligent networked control system scheme of roller kiln is proposed in the paper. Flame image recognition method of simulating artificial-look-fire is proposed for the auxiliary detection of roller kiln burning zone conditions so as to improve the real-time detection accuracy of roller kiln burning zone and the control effect. The hardware and software platform of the system are also designed. The system proposed is mainly composed of six module such as key process detection module, flame process module, recognition model for burning zone, industrial control computer, network transmission module, and actuators. System software is mainly composed of five modules. In conclusion, the paper provides a kind of new method and new idea for research and design on flame image processing-based intelligent networked control system of roller kiln.

• Periodical: Advanced Materials Research (Volumes 546-547)
• Pages: 612-616
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.546-547.612
• Online since: July 2012
• Authors: Yong Hong Zhu, Jian Hong Wang
• Keywords: Data Fusion, Flame Image Recognition, Networked Control System (NCS), Roller Kiln

References

[1] S. T. Li, Y. N. Wang and C. F. Zhang: Neural network control system for rotary kiln based on features of combustion flame. Acta Automatica Sinica（China), vol. 4(2003), pp.11-12.

[2] C. G. Zhen: Research on detection method of furnace flame based on image processing technology. North China Electric Power University (China) , Ph.D. Thesis, (2004).

[3] W. III Phillips, M. Shah, and N. Da Vitoria Lobo: Flame recognition in video. Pattern Recognition Letters, vol. 23 (2002), pp.319-327.

DOI: 10.1016/s0167-8655(01)00135-0

[4] P. SUN, T. Y. CHAI, and X. J. ZHOU: Flame image recognition system for alumina rotary kiln burning zone. Journal o f Chemical Industry and Engineering(China), vol. 59(2008), pp.1839-1842.

[5] H. P. Dai, Q. J. Dai: Building and practice of control network for PLC ceramic industrial kiln. Automation and Instrumentation (China), Vol 107(2003), pp.50-53.

[6] W. L. Cai, S. C. Chen, and D. Q. Zhang: Fast and robust fuzzy C-means clustering algorithms incorporation local information for image segmentation. Pattern Recognition, vol. 40(2007), pp.825-838.

DOI: 10.1016/j.patcog.2006.07.011

[7] T. W. Cheng, D. B. Goldgof and L. Q. Hall: Fast fuzzy clustering. Fuzzy Sets and System, vol, 93(1998), pp.49-56.

DOI: 10.1016/s0165-0114(96)00232-1

[8] G. Cena and A. Valenzano: An improved CAN fieldbus for industrial application,. IEEE Trans. on Industrial Electronic, vol 44(1997), pp.533-564.

DOI: 10.1109/41.605633

[9] A. M. Tekalp: Digital video processing. New Jersey: Prentice Hall, (1995).

[10] A. M. Pan: The theory and priciple of COM. Beijing, Tsinghua University Press, (1998).

[11] F. Z. Xue, S. R. Lin, and Y. Tang: Research and development of client application based on OPC data access specification. Computer Engineering(China), vol. 28(2002), pp.229-231.