Expert System for Sintering Process Control based on the Information about solid-fuel Flow Composition

Usually, the solid-fuel flow of an iron ore sinter plant consists of different types of the solid-fuels, which differ from each other. Information about the composition of the solid-fuel flow usually comes every 8-24 hours. It can be clearly seen that this information cannot be used to control the sintering process in real time. Due to this, we propose an expert system which uses indirect measurements from the process in order to obtain the composition of the solid-fuel flow by solving an optimization task. Then this information can be used to control the sintering process. The proposed technique can be successfully used to improve sinter quality and reduce the amount of solid-fuel used by the process.

Authors:

• Yendiyarov Sergei
• Zobnin Boris
• Petrushenko Sergei

Keywords:

• sintering process
• particle swarm optimization
• optimal control
• expert system
• solid-fuel

References:

[1] Kostial I., Doreak L., Terpak J. Optimal control of the sintering process
// Proceedings of the 16th IFAC World Congress, vol. 16, part 1, Czech
Republic, 2005.
[2] Xiao-hui F., Lijuan J., Xu-ling C. Air Leakage Online Monitoring and
Diagnosis Model for Sintering // 2012 TMS Annual Meeting &
Exhibition, 3rd International Symposium on High Temperature
Metallurgical Processing, United States, Florida, 2012.
[3] Dai-fei L., Xu-ling C. Development and Application of Sintering
Process Data Mining System // Management and Service Science,
Wuhan, 2009, pp. 1-4.
[4] Xu-ling C., Xiao-hui F., Tao J. Operation Guidance System for Iron Ore
Sintering Process // Intelligent System Design and Engineering
Application, Changsha, 2010, pp. 1053-1055
[5] Xiang J., Wu M. Intelligent Integrated Optimization Control Design of
Comprehensive Production Indices for Sintering Process // Control
Conference, China, 2007, pp. 750-754.
[6] Langer M., Vogel B. Synthesis of plantwide quality prediction system
for a sintering plant // 15th Triennial World Congress, Barcelona, Spain,
vol. 15, Part 1, 2002.
[7] Xiao-hui F., Xu-ling C., Wang Y. Expert System for Sintering Process
Control // Expert systems, Croatia, 2010, pp. 65-90.
[8] Xiao-hui F., Xu-ling C., Tao J. Real-time operation guide system for
sintering process with artificial intelligence // Journal of central south
university of technology, China, vol. 12, issue 5, 2005, pp. 531-535.
[9] Zhuwu M. Automation for Ironmaking, Metallurgy Industry Press, ISBN
7-5024-3639-1, Beijing, 2005.
[10] Korotich V. I., Frolov Yu. A. Sintering of the granular materials (in
Russian) - Yekaterinburg, UGTU-UPI, 2003, p. 400.
[11] Kleyn, V. I. Heat engineering methods of analysis of the sintering
process (in Russian). - Yekaterinburg: UGTU-UPI, 2004. p. 224.
[12] Kulkarni A.D., Cavanaugh C. Fuzzy Neural Network Models for
Classification // Applied Intelligence, USA, vol. 18, N. 3, March, 2000,
pp. 207-215.
[13] Kennedy J., Eberhart R. C. Swarm intelligence: collective, adaptive,
Academic Press, USA, San Francisco, 2001, pp. 541.
[14] Singiresu S. R. Engineering Optimization: Theory and Practice, A John
Wiley & Sons, Inc., New York, 2009, pp. 710.
[15] Zobnin B., Yendiyarov S., Petrushenko S. Design of a wavelet based
data mining technique // Buletinul Institutului Politehnic din Iaşi,
Automatic Control and Computer Science Section, Romania, Issue 1,
March, 2012, pp. 22-37.
[16] Yendiyarov S., Petrushenko S. Robust Probabilistic Online Change
detection Algorithm based on the Continuous Wavelet Transform //
World Academy of Science, Engineering and Technology, France, Issue
60, December 2011, pp. 1810-1814.
[17] Yendiyarov S., Zobnin B., Petrushenko S. Online Change Detection
Algorithm Based on the Continuous Wavelet Transform, the CUSUM
Algorithm and an Autoregressive Model // Current Trends in Signal
Processing, India, vol. 1, Issue 2-3, November 2011, pp. 7-18.