single-jc.php

JACIII Vol.24 No.2 pp. 221-231
doi: 10.20965/jaciii.2020.p0221
(2020)

Paper:

Assessment of Coke Oven Operating State Using Trend Analysis and Information Entropy

Qi Lei and Fengmei Guo

School of Automation, Central South University
932 Lushan Road, Changsha, Hunan 410083, China

Received:
December 31, 2019
Accepted:
January 30, 2020
Published:
March 20, 2020
Keywords:
coke oven, operating state assessment, qualitative trend analysis, information entropy
Abstract

In the combustion process of a coke oven, it is crucial to evaluate the operating state to ensure control performance for the stabilization of the coke oven temperature. This paper presents an assessment method for a coke oven operating state based on the analysis of the mechanism. A coke oven, which is an integrator, is categorized into serial subsystems, which include two coking chambers and one combustion chamber. First, the raw gas temperature of every coking chamber is extracted online and is combined with the qualitative trend analysis that yields the feature point of the raw gas temperature. Subsequently, fuzzy method is presented to describe the uncertainty and evaluate the heat level of each subsystem. Finally, a comprehensive assessment of the operating state of the coke oven is performed by combining the weighted contribution of all subsystems, which is expressed by information entropy. Simulations and experiments demonstrate the validity of the method.

Cite this article as:
Q. Lei and F. Guo, “Assessment of Coke Oven Operating State Using Trend Analysis and Information Entropy,” J. Adv. Comput. Intell. Intell. Inform., Vol.24 No.2, pp. 221-231, 2020.
Data files:
References
  1. [1] N. K. Berkutov, Y. V. Stepanov, N. K. Popova, Y. P. Petrenko, and V. V. Belov, “The relation between coke quality and blast-furnace performance,” Steel in Translation, Vol.37, No.5, pp. 438-441, 2007.
  2. [2] N. I. Yurin, O. M. Morozov, O. L. Likhacheva, V. I. Yukhimenko, and S. A. Shtekker, “Influence of coke quality on blast-furnace performance,” Steel in Translation, Vol.41, No.11, pp. 924-927, 2011.
  3. [3] Y. Yao and F. Gao, “A survey on multistage/multiphase statistical modeling methods for batch processes,” Annual Reviews in Control, Vol.33, No.2, pp. 172-183, 2009.
  4. [4] Y. Hui and X. Zhao, “Multi-phase batch process monitoring based on multiway weighted global neighborhood preserving embedding method,” J. of Process Control, Vol.69, pp. 44-57, 2018.
  5. [5] Z. Lv, X. Yan, and Q. Jiang, “Batch process monitoring based on self-adaptive subspace support vector data description,” Chemometrics and Intelligent Laboratory Systems, Vol.170, pp. 25-31, 2017.
  6. [6] R. Guo, K. Guo, and J. Dong, “Phase partition and online monitoring for batch process based on multiway BEAM,” IEEE Trans. on Automation Science and Engineering, Vol.14, No.4, pp. 1582-1589, 2016.
  7. [7] Y. Zhang and S. Li, “Modeling and monitoring between-mode transition of multimodes processes,” IEEE Trans. on Industrial Informatics, Vo.9, No.4, pp. 2248-2255, 2013.
  8. [8] A. Tulsyan, C. Garvin, and C. Ündey, “Advances in industrial biopharmaceutical batch process monitoring: Machine-learning methods for small data problems,” Biotechnology and Bioengineering, Vol.115, No.8, pp. 1915-1924, 2018.
  9. [9] K. Villez, C. Rosen, F. Anctil, C. Duchesne, and P. A. Vanrolleghem, “Qualitative Representation of Trends (QRT): Extended method for identification of consecutive inflection points,” Computers & Chemical Engineering, Vol.48, pp. 187-199, 2013.
  10. [10] B. Zhou and H. Ye, “A study of polynomial fit-based methods for qualitative trend analysis,” J. of Process Control, Vol.37, pp. 21-33, 2016.
  11. [11] M. R. Maurya, P. K. Paritosh, R. Rengaswamy, and V. Venkatasubramanian, “A framework for on-line trend extraction and fault diagnosis,” Engineering Applications of Artificial Intelligence, Vol.23, No.6, pp. 950-960, 2010.
  12. [12] S. Dash, M. R. Maurya, V. Venkatasubramanian, and R. Rengaswamy, “A novel interval-halving framework for automated identification of process trends,” AIChE J., Vol.50, No.1, pp. 149-162, 2004.
  13. [13] F. I. Gamero, J. Meléndez, and J. Colomer, “Process diagnosis based on qualitative trend similarities using a sequence matching algorithm,” J. of Process Control, Vol.24, No.9, pp. 1412-1424, 2014.
  14. [14] S. Dash, R. Rengaswamy, and V. Venkatasubramanian, “Fuzzylogic based trend classification for fault diagnosis of chemical processes,” Computers & Chemical Engineering, Vol.27, No.3, pp. 347-362, 2003.
  15. [15] C. Spandagos and T. L. Ng, “Fuzzy model of residential energy decision-making considering behavioral economic concepts,” Applied Energy, Vo.213, pp. 611-625, 2018.
  16. [16] A. Mardani, E. K. Zavadskas, D. Streimikiene, A. Jusoh, K. M. Nor, and M. Khoshnoudi, “Using fuzzy multiple criteria decision making approaches for evaluating energy saving technologies and solutions in five star hotels: A new hierarchical framework,” Energy, Vol.117, pp. 131-148, 2016.
  17. [17] C. Franco, M. Bojesen, J. L. Hougaard, and K. Nielsen, “A fuzzy approach to a multiple criteria and Geographical Information System for decision support on suitable locations for biogas plants,” Applied Energy, Vol.140, pp. 304-315, 2015.
  18. [18] M. Zarghami, R. Ardakanian, A. Memariani, and F. Szidarovszky, “Extended OWA operator for group decision making on water resources projects,” J. of Water Resources Planning and Management, Vol.134, No.3, pp. 266-275, 2008.
  19. [19] N. Agell, M. Sánchez, F. Prats, and L. Roselló, “Ranking multiattribute alternatives on the basis of linguistic labels in group decisions,” Information Sciences, Vol.209, pp. 49-60, 2012.
  20. [20] C. Kobashikawa, Y. Hatakeyama, F. Dong, and K. Hirota, “Fuzzy algorithm for group decision making with participants having finite discriminating abilities,” IEEE Trans. on Systems, Man, and Cybernetics–Part A: Systems and Humans, Vol.39, No.1, pp. 86-95, 2008.
  21. [21] T. Baležentis and S. Zeng, “Group multi-criteria decision making based upon interval-valued fuzzy numbers: an extension of the MULTIMOORA method,” Expert Systems with Applications, Vol.40, No.2, pp. 543-550, 2013.
  22. [22] S.-J. Chuu, “A fuzzy multi-granularity linguistic approach under group decision-making for the evaluation of supply chain flexibility,” J. of Industrial and Production Engineering, Vol.31, No.6, pp. 303-322, 2014.
  23. [23] Y. Minatour, H. Bonakdari, M. Zarghami, and M. A. Bakhshi, “Water supply management using an extended group fuzzy decisionmaking method: a case study in north-eastern Iran,” Applied Water Science, Vol.5, No.3, pp. 291-304, 2015.
  24. [24] M. Gupta and B. K. Mohanty, “An algorithmic approach to group decision making problems under fuzzy and dynamic environment,” Expert Systems with Applications, Vol.55, pp. 118-132, 2016.
  25. [25] Y. Wang, Y. Y. Tang, and L. Li, “Robust face recognition via minimum error entropy-based atomic representation,” IEEE Trans. on Image Processing, Vol.24, No.12, pp. 5868-5878, 2015.
  26. [26] G. Beruvides, R. Quiza, and R. E. Haber, “Multi-objective optimization based on an improved cross-entropy method. A case study of a micro-scale manufacturing process,” Information Sciences, Vol.334-335, pp. 161-173, 2016.
  27. [27] M. Wu, Q. Lei, W. Cao, and J. She, “Integrated soft sensing of coke-oven temperature,” Control Engineering Practice, Vol.19, No.10, pp. 1116-1125, 2011.
  28. [28] Z. D. Yu and C. Y. Cai, “Coking Technology,” Metallurgical Industry Press, 2005.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Nov. 04, 2024