SCIENCE CHINA Technological Sciences, Volume 59 , Issue 4 : 647-656(2016) https://doi.org/10.1007/s11431-016-6020-7

Evolved clustering analysis of 300 MW boiler furnace pressure sequence based on entropy characterization

More info
  • AcceptedJan 11, 2016
  • PublishedMar 25, 2016


The furnace process is very important in boiler operation, and furnace pressure works as an important parameter in furnace process. Therefore, there is a need to analyze and monitor the pressure signal in furnace. However, little work has been conducted on the relationship with the pressure sequence and boiler's load under different working conditions. Since pressure sequence contains complex information, it demands feature extraction methods from multi-aspect consideration. In this paper, fuzzy c-means analysis method based on weighted validity index (VFCM) has been proposed for the working condition classification based on feature extraction. To deal with the fluctuating and time-varying pressure sequence, feature extraction is taken as nonlinear analysis based on entropy theory. Three kinds of entropy values, extracted from pressure sequence in time-frequency domain, are studied as the clustering objects for work condition classification. Weighted validity index, taking the close and separation degree into consideration, is calculated on the base of Silhouette index and Krzanowski-Lai index to obtain the optimal clustering number. Each time FCM runs, the weighted validity index evaluates the clustering result and the optimal clustering number will be obtained when it reaches the maximum value. Four datasets from UCI Machine Learning Repository are presented to certify the effectiveness in VFCM. Pressure sequences got from a 300 MW boiler are then taken for case study. The result of the pressure sequence case study with an error rate of 0.5332% shows the valuable information on boiler's load and pressure sequence in furnace. The relationship between boiler's load and entropy values extracted from pressure sequence is proposed. Moreover, the method can be considered to be a reference method for data mining in other fluctuating and time-varying sequences.


[1] Lee C L, Jou C J G. Improving furnace energy efficiency through adjustment of damper angle. Int J Hydrogen Energ, 2013, 38: 2504- 2509. Google Scholar

[2] Luo Z, Zhou H C. A combustion-monitoring system with 3-D temperature reconstruction based on flame-image processing technique. IEEE T Instrum Meas, 2007, 56: 1877-1882. Google Scholar

[3] Zhou H C, Lou C, Cheng Q, et al. Experimental investigations on visualization of three-dimensional temperature distributions in a large-scale pulverized-coal-fired boiler furnace. P Combust Inst, 2005, 30: 1699-1706. Google Scholar

[4] Zhou Y G, Xu T M, Hui S E. Experimental and numerical study on the flow fields in upper furnace for large scale tangentially fired boilers. Appl Therm Eng, 2009, 29,732-739. Google Scholar

[5] Zanoli, S M, Barchiesi D, Astolfi G, et al. Advanced control solutions to increase efficiency of a furnace combustion process. In: European Control Conference (ECC), Switzerland: IEEE, 2013. 4316-4321. Google Scholar

[6] Estes M J, Sappey A D, Hofvander H, et al. Method and apparatus for monitoring combustion properties in an interior of a boiler. U.S. Patent 8,786,856. 2014-7-22. Google Scholar

[7] Piskova E, Morl L. Characterization of spouted bed regimes using pressure fluctuation signals. Chem Eng Sci, 2008, 63: 2307-2316. Google Scholar

[8] Srdjan S, Leckner B, Johnsson F. Characterization of fluid dynamics of fluidized beds by analysis of pressure fluctuations. Prog Energ Combust, 2007, 33: 453-496. Google Scholar

[9] Huang B, Luo Z, Zhou H. Optimization of combustion based on introducing radiant energy signal in pulverized coal-fired boiler. Fuel Process Technol, 2010, 91: 660-668. Google Scholar

[10] Vikhansky A, Barziv E, Chudnovsky B, et al. Measurements and numerical simulations for optimization of the combustion process in a utility boiler. Int J Energ Res, 2004, 28: 391-401. Google Scholar

[11] Ma S H, Hua Y, Li X B. An analysis of flame signals in a boiler furnace based on a phase space reconstruction. J Engin Therm Energ Pow, 2007, 22: 440-442. Google Scholar

[12] Ronquillo G, Romero C E, Yao Z, et al. On-line flame signal time series analysis for oil-fired burner optimization. Fuel, 2015, 58: 416- 423. Google Scholar

[13] Dıéz L, Cortes C, Arauzo I, et al. Combustion and heat transfer monitoring in large utility boilers. Int J Therm Sci, 2001, 40: 489-496. Google Scholar

[14] Lim G P, Hur K B, Park D Y, et al. The development of boiler furnace pressure control algorithm and distributed control system for coal-fired power plant. T Korean Inst Electric Eng P, 2013, 62: 117-126. Google Scholar

[15] Zhong W, Zhang M. Characterization of dynamic behavior of a spout-fluid bed with Shannon entropy analysis. Powder Technol, 2005, 159: 121-126. Google Scholar

[16] Hajmeer M, Basheer I. A probabilistic neural network approach for modeling and classification of bacterial growth/nogrowth data. J Microbiol Meth, 2002, 51: 217-226. Google Scholar

[17] Rutkowski L. Adaptive probabilistic neural network for pattern classification in time-varying environment. IEEE T Neural Networ, 2004, 15: 811-827. Google Scholar

[18] Teng Y Y, Chen J C, Lu C W, et al. Effects of the furnace pressure on oxygen and silicon oxide distributions during the growth of multi crystalline silicon ingots by the directional solidification process. J Cryst Growth, 2011, 318: 224-229. Google Scholar

[19] Chen M S, Han J, Yu P S. Data mining: an overview from a database perspective. IEEE T Knowl Data En, 1996, 8: 866-883. Google Scholar

[20] Liao T W. Clustering of time series data-a survey. Pattern Recogn, 2005, 38: 1857-1874. Google Scholar

[21] Bishnu P S, Bhattacherjee V. Software fault prediction using quad tree-based k-means clustering algorithm. IEEE T Knowl Data En, 2012, 24: 1146-1150. Google Scholar

[22] Zadeh L A. Fuzzy sets. Inform Contr, 1965, 8: 338-353. Google Scholar

[23] Bezdek J C, Ehrlich R. Full W. FCM: The fuzzy c-means clustering algorithm. Comput Geosci-UK, 1984, 10: 191-203. Google Scholar

[24] Agustin L E, Salcedo S, Jimenez S, et al. A new grouping genetic algorithm for clustering problems. Expert Syst Appl, 2012, 39: 9695- 9703. Google Scholar

[25] Samal N R, Konar A, Das S, et al. A closed loop stability analysis and parameter selection of the particle swarm optimization dynamics for faster convergence. In: IEEE Congress on Evolutionary Computation. Singapore: IEEE, 2007. 1769-1776. Google Scholar

[26] Nasir M, Das S, Maity D, et al. A dynamic neighborhood learning based particle swarm optimizer for global numerical optimization. Inform Sci, 2012, 209: 16-36. Google Scholar

[27] Forsati R, Keikha A, Shamsfard M, et al. An improved bee colony optimization algorithm with an application to document clustering. Neurocomputing, 2015, 159: 9-26. Google Scholar

[28] Bagirov A M, Mohebi E. Nonsmooth Optimization Based Algorithms in Cluster Analysis. Partitional Clustering Algorithms. Berlin: Springer International Publishing, 2015: 99-146. Google Scholar

[29] Cura T. A particle swarm optimization approach to clustering. Expert Syst Appl, 2012, 39: 1582-1588. Google Scholar

[30] Rousseeuw P. Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math, 1987, 20: 53-65. Google Scholar

[31] Davies D, Bouldin D. A cluster separation measure. IEEE T Pattern Anal Mach Intell, 1979, 1: 224-227. Google Scholar

[32] Caliński T, Harabasz J. A dendrite method for cluster analysis. Commun Stat-Theor M, 1974, 3: 1-27. Google Scholar

[33] Krzanowski W J, Lai Y T. A criterion for determining the number of groups in a data set using sum-of-squares clustering. Biometrics, 1988: 23-34. Google Scholar

[34] Srinivasan V, Eswaran C, Sriraam N. Artificial neural network based epileptic detection using time-domain and frequency-domain features. J Med Syst, 2005, 29: 647-660. Google Scholar

[35] Zhang Y L. Zhang Q Y, Melodia T. A frequency-domain entropy-based detector for robust spectrum sensing in cognitive radio networks. IEEE Commun Lett, 2010, 14: 533-535. Google Scholar

[36] Vakkuri A, Ylihankala A, Talja P, et al. Time-frequency balanced spectral entropy as a measure of anesthetic drug effect in central nervous system during sevoflurane, propofol, and thiopental anesthesia. Acta Anaesth Scand, 2004, 48: 145-153. Google Scholar

[37] Asuncion A, Newman D. UCI machine learning repository. 2007.. Google Scholar

Copyright 2020 Science China Press Co., Ltd. 《中国科学》杂志社有限责任公司 版权所有

京ICP备17057255号       京公网安备11010102003388号