Skip to main content

Parabola-Based Flue Gas Temperature Modeling and Its Application in BTP Control of a Sintering Process

  • Conference paper
  • First Online:
  • 1276 Accesses

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 404))

Abstract

It is very important to predict the accurate position of the burning through point (BTP) in the sintering process. When BTP is controlled accurately, the energy consumption in the sintering process can be reduced greatly. Although BTP cannot be measured directly, we can measure the flue gas temperature to predict BTP. When the flue gas temperature of the twenty-third bellow is controlled at 600 °C, BTP will be controlled on the center of the twenty-third bellow. In this case, the sinter mix can be converted into the sinter ore with the maximum conversion rate. A method of modeling the flue gas temperature based on parabola is discussed in the paper. By means of the least square method (LSM), the relationship between the flue gas temperature and the negative pressure is modeled. The position of the burning through point (BTP) can be controlled by adjusting the negative pressure of the motor which can be controlled by adjusting the duty cycle. By comparing the measured flue gas temperature with the set temperature and comparing the measured negative pressure with the set negative pressure, the fuzzy controller with 81 rules can output the appropriate duty cycle which can control the motor properly. The flue gas temperature of the twenty-third bellow is checked so that the real position of the burning through point can be obtained. Simulations show that the position of the burning through point in the sintering process can be controlled exactly.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Wang D, Liu DR, Wei QL, Zhao DB, Jing N (2012) Optimal control of unknown nonaffine nonlinear discrete-time systems based on adaptive dynamic programming. Automatica 48:1825–1832

    Google Scholar 

  2. Li SY, Hu CF (2007) Two-step interactive satisfactory method for fuzzy multiple objective optimization with preemptive priorities. IEEE Trans Fuzzy Syst 15(3):417–425

    Article  Google Scholar 

  3. Umadevi T, Brahmacharyulu A, Karthik P, Mahapatra PC, Prabhu M, Ranjan M (2012) Ironmaking Steelmaking 39(3):222–227

    Article  Google Scholar 

  4. de Castro JA, Sazaki Y, Yagi J-i (2012) Three dimensional model of the iron ore sintering process based on multiphase theory. Mater Res 15(6):848–858

    Google Scholar 

  5. Zhao Jia P, Loo Chin E, Dukino Rodney D (2015) Modelling fuel combustion in iron ore sintering. Combust Flame 162:1019–1034

    Article  Google Scholar 

  6. Chen XL, Fan XH, Wang Y, Long HM, Jiang T, Shi J et al (2009) Control guidance system for sintering burn through point. Ironmaking Steelmaking 36(3):209–211

    Article  Google Scholar 

  7. ChengWS (2006) Prediction system of burning through point(BTP) based on adaptive pattern clustering and feature map. In: Proceedings of 2006 international conference machine learning and cybernetics. pp 3089–3094

    Google Scholar 

  8. Kim YH, Kwon WH (1998) An application of min-max generalized predictive control to sintering processes. Control Eng Pract 6(8):999–1007

    Article  Google Scholar 

  9. Soyguder S, Alli H (2010) Fuzzy adaptive control for the actuators position control and modeling of an expert system. Expert Syst Appl 37(3):2072–2080

    Article  Google Scholar 

  10. Zhang JH, Xie AG, Shen FM (2007) Multi-objective optimization and analysis model of sintering process based on BP neural network. J Iron Steel Res 14(2):1–5

    Article  Google Scholar 

Download references

Acknowledgments

This work is supported by the National Natural Science Foundation of China under Grant No. 61273138, 61573197, the National Key Technology R&D Program under Grant No. 2015BAK06B04, the key Fund of Tianjin under Grant No. 14JCZDJC39300 and the key Technologies R&D Program of Tianjin under Grant No. 14ZCZDSF00022.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qinglin Sun .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer Science+Business Media Singapore

About this paper

Cite this paper

Liu, S., Sun, Q., Ma, C. (2016). Parabola-Based Flue Gas Temperature Modeling and Its Application in BTP Control of a Sintering Process. In: Jia, Y., Du, J., Zhang, W., Li, H. (eds) Proceedings of 2016 Chinese Intelligent Systems Conference. CISC 2016. Lecture Notes in Electrical Engineering, vol 404. Springer, Singapore. https://doi.org/10.1007/978-981-10-2338-5_40

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-2338-5_40

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2337-8

  • Online ISBN: 978-981-10-2338-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics