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SCIENCE CHINA Information Sciences, Volume 61 , Issue 11 : 110204(2018) https://doi.org/10.1007/s11432-018-9601-y

Energy consumption diagnosis in the iron and steel industry via the Kalman filtering algorithm with a data-driven model

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  • ReceivedJun 7, 2018
  • AcceptedSep 6, 2018
  • PublishedOct 19, 2018

Abstract

There is no abstract available for this article.


Acknowledgment

This work was partly supported by National Key Research and Development Plan (Grant No. 2016YFB0901900) and National Natural Science Foundation of China (Grant Nos. 71302161, 61374203).


References

[1] Gopalakrishnan B, Mate A, Mardikar Y, et al. Energy efficiency measures in the wood manufacturing industry. In: Proceedings of 2005 ACEEE Summer Study on Energy Efficiency in Industry, 2005. 1-68--1-76. Google Scholar

[2] Usón S, Valero A, Correas L. Energy efficiency assessment and improvement in energy intensive systems through thermoeconomic diagnosis of the operation. Appl Energy, 2010, 87: 1989-1995 CrossRef Google Scholar

[3] Saidur R, Mekhilef S. Energy use, energy savings and emission analysis in the Malaysian rubber producing industries. Appl Energy, 2010, 87: 2746-2758 CrossRef Google Scholar

[4] Kalman R E. A new approach to linear filtering and prediction problems. J Basic Eng, 1960, 82D: 35--45. Google Scholar

[5] Yan L, Xia Y, Fu M. Optimal fusion estimation for stochastic systems with cross-correlated sensor noises. Sci China Inf Sci, 2017, 60: 120205 CrossRef Google Scholar

[6] Sohlberg B. Monitoring and failure diagnosis of a steel strip process. IEEE Trans Contr Syst Technol, 1998, 6: 294-303 CrossRef Google Scholar

[7] Cao M, Qiu Y, Feng Y. Study of Wind Turbine Fault Diagnosis Based on Unscented Kalman Filter and SCADA Data. Energies, 2016, 9: 847 CrossRef Google Scholar

[8] Wen C L, Qiu A B, Jiang B. An output delay approach to fault estimation for sampled-data systems. Sci China Inf Sci, 2012, 55: 2128-2138 CrossRef Google Scholar

[9] Welch G, Bishop G. An Introduction to the Kalman Filter. University of North Carolina at Chapel Hill, TR 95-041. 2006. Google Scholar

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