SCIENTIA SINICA Informationis, Volume 48, Issue 7: 856-870(2018) https://doi.org/10.1360/N112017-00300

Identification method for furnace flame based on adaptive color model

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  • ReceivedFeb 27, 2018
  • AcceptedMar 18, 2018
  • PublishedJul 16, 2018


A kiln and boiler combustion system is a complex nonlinear system. The method of an adaptive color model for furnace flame recognition is proposed to improve real-time flame detection and the recognition effect of combustion flames.First, a variable adaptive color model was designed by using the combustion characteristics of furnace flames. The expression of the threshold in the adaptive color model was deduced by the method of maximum classes square error. Based on this, the human-learning optimization algorithm was used to solve the optimal segmentation threshold. Then, fast and accurate identification of the combustion conditions of furnace flames was achieved. Simulation results are presented to verify the feasibility and effectiveness of the proposed results.

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  • Figure 1

    (Color online) The effect diagram of the combustion flame in different scenes. Flame image (a) I, (b) II, (c) III, (d) IV

  • Figure 2

    (Color online) The 3D histograms of color components of combustion flame image I. (a) Combustion flame image I; (b) red component; (c) green component; (d) blue component

  • Figure 3

    (Color online) The 3D histograms of color components of combustion flame image II. (a) Combustion flame image II; (b) red component; (c) green component; (d) blue component

  • Figure 4

    (Color online) The 3D histograms of color components of combustion flame image III. (a) Combustion flame image III; (b) red component; (c) green component; (d) blue component

  • Figure 5

    (Color online) The 3D histograms of color components of combustion flame image IV. (a) Combustion flame image IV; (b) red component; (c) green component; (d) blue component

  • Figure 6

    The flow diagram of the flame recognition algorithm base on adaptive color model

  • Figure 7

    (Color online) Ten flame images of furnace combustion. Original image (a) I, (b) II, (c) III, (d) IV, (e) V,protect łinebreak (f) VI, (g) VII, (h) VIII, (i) IX, (j) X

  • Figure 8

    (Color online) Comparison of fitness value in original image I. (a) Genetic algorithm; (b) particle swarm optimization; (c) human learning optimization

  • Figure 9

    (Color online) Comparison of fitness value in original image II. (a) Genetic algorithm; (b) particle swarm optimization; (c) human learning optimization

  • Figure 10

    (Color online) Comparison result of flame identification in original image I. (a) Original image I; (b) Ref. [8]; (c) Ref. [11]; (d) proposed method

  • Figure 11

    (Color online) Comparison result of flame identification in original image II. (a) Original image II; (b) Ref. [8]; (c) Ref. [11]; (d) proposed method

  • 1   Table 1Comparison of flame identification of different algorithms
    Algorithms Threshold $t_1$ Threshold $t_2$ Fitness value Identification time (s)
    Original image I GA+proposed model 185 101 9.2733E+03 0.3748
    PSO+proposed model 178 97 9.2862E+03 0.5097
    HLO+proposed model 178 97 9.2862E+03 0.0935
    Original image II GA+proposed model 151 124 1.4230E+04 0.4757
    PSO+proposed model 160 129 1.4246E+04 0.6128
    HLO+proposed model 160 129 1.4246E+04 0.1626
  • 2   Table 2Comparison of flame identification in different scenes
    Method Correct pixels Total pixels Identification accuracy (%) Identification time (s)
    Original image I Ref. [8] 33222 43621 76.16 0.2747
    Ref. [11] 42651 43621 97.78 0.1702
    Proposed method 42823 43621 98.17 0.0935
    Original image II Ref. [8] 266452 284200 93.76 0.4251
    Ref. [11] 276770 284200 97.39 0.2675
    Proposed method 280521 284200 98.71 0.1626
    Original image III Ref. [8] 587651 917631 64.04 0.7643
    Ref. [11] 835341 917631 91.03 0.6568
    Proposed method 849676 917631 92.59 0.4736
    Original image IV Ref. [8] 81957 108747 75.36 0.2065
    Ref. [11] 81413 108747 74.86 0.1950
    Proposed method 101054 108747 92.93 0.0971
    Original image V Ref. [8] 670752 972873 68.95 0.9168
    Ref. [11] 963243 972873 97.77 0.7279
    Proposed method 961911 972873 98.87 0.5164
    Original image VI Ref. 8 474027 782595 60.57 0.7238
    Ref. 11 759863 782595 97.10 0.5636
    Proposed method 774106 782595 98.92 0.3556
    Original image VII Ref. [8] 23141 28400 81.48 0.1513
    Ref. [11] 20451 28400 72.01 0.1595
    Proposed method 27549 28400 97.00 0.0813
    Original image VIII Ref. [8] 92974 97660 95.20 0.1765
    Ref. [11] 89838 97660 91.99 0.1810
    Proposed method 94374 97660 96.64 0.0977
    Original image IX Ref. [8] 539152 735232 73.33 0.6835
    Ref. [11] 393618 735232 94.34 0.5329
    Proposed method 701257 735232 95.38 0.3324
    Original image X Ref. [8] 22704 24928 91.08 0.1305
    Ref. [11] 19890 24928 79.79 0.1596
    Proposed method 23973 24928 96.17 0.0796

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