Information-Entropy-Based Integrated Model for Predicting Burn-Through Point in Lead-Zinc Sintering Process

Chun Sheng Wang

doi:10.4028/www.scientific.net/AMR.396-398.40

Paper Titles

Research on Wear Status Diagnosis for Aeroengine Based on Spectrography and Ferrography
p.24

Effect of Metal Ions (Cu²⁺/Fe²⁺) on the Antioxidant Activity of L-Ascorbic Acid-Glycine Model System
p.28

Study on the Pretreatment of Seawater to Reduce Turbidity and Boron Using Different Alkalizer
p.32

Synthesis and Characterization of a New Type of Smart Hydroxyapatite-PNIPAM Hybrid Nanopatrticles
p.35

Information-Entropy-Based Integrated Model for Predicting Burn-Through Point in Lead-Zinc Sintering Process
p.40

Extraction of Feature Photoacoustic Spectroscopy Probed by a Sensor against a Large Background Noise
p.44

Zinc and Cobalt Recovery from Co-Ni Residue of Zinc Hydrometallurgy by an Ammonia Process
p.48

Studies on the Antioxidant Activities of Total Alkaloids from the Fruits of Evodia rutaecarpa (Juss.) Benth
p.52

Investigation on the Synthesize Reaction Possibility of M(CO)₂CY-Ph₃XR(M=Cr, Mn; X=Sn, Ge; Y=S, Se; R= N(C₄H₄), N(C₈H₆), C₆H₅)
p.56

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 396-398Information-Entropy-Based Integrated Model for...

Information-Entropy-Based Integrated Model for Predicting Burn-Through Point in Lead-Zinc Sintering Process

Abstract:

This paper presents an information-entropy-based integrated model for predicting the burn-through point (BTP) in lead-zinc sintering process. First, a fuzzy T-S prediction model for BTP was established to deal with the uncertainty of the vertical burning speed. Considering the BTP is also affected by process parameters, a neural network (NN) prediction model for BTP was then built. Finally, an integrated model for predicting the BTP was constructed by combining the above two models using the recursive entropy algorithm. The practical running results demonstrate the validity of the proposed integrated predictive model.

You might also be interested in these eBooks

Advances in Chemical Engineering: ICCMME 2011

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 396-398)

Pages:

40-43

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.396-398.40

Citation:

Cite this paper

Online since:

November 2011

Authors:

Chun Sheng Wang

Keywords:

Burn-Through Point, Entropy, Integrated Predictive Model, Lead-Zinc Sintering Process

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] S. H. Lee, C. B. Yoon, S. M. Lee and H. E. Kim: Journal of the European Ceramic Society Vol. 26 (2006), pp.111-115.

Google Scholar

[2] W. S. Cheng: In Proceeding of the 2006 International Conference on Machine Learning and Cybernetics, 2006, pp.3356-3360.

Google Scholar

[3] M. Wu, C. H. Xu, J. H. She and R. Yokoyama: Control Engineering Practice Vol. 17 (2009), pp.280-290.

Google Scholar

[4] L. Peng, Z. C. Ji and T. D. Tan: In Proceedings of the 2005 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2005, pp.1385-1388.

Google Scholar

[5] R. Feng, H.R. Zhang and H. H. Shao: Information and Control Vol. 31 (2002), pp.567-571.

Google Scholar

[6] J. Terpark, L. Dorcak, I. Kostial and L. Pivka: Metalurgija Vol. 44 (2005), pp.281-284.

Google Scholar

[7] A. Giaquinto, G. Fornarelli, G. Brunetti and G. Acciani: IEEE Transactions on Industrial Informatics Vol. 5 (2009), pp.56-66.

DOI: 10.1109/tii.2008.2008640

Google Scholar

[8] J. Terpak, L. Dorcak, I. Kostial and L. Pivka: Control of burn-through point for agglomeration belt. Metalurgija, Vol. 44 (2005), pp.281-284.

Google Scholar

[9] M. Wu, C. H. Xu and Y. X. Du: Transactions of Nonferrous Metals Society of China Vol. 16 (2006), pp.975-981.

Google Scholar