基于自适应颜色模型的炉膛火焰识别方法
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摘要
炉窑和锅炉燃烧系统是一个复杂的非线性系统.针对当前炉膛火焰燃烧状况识别效果不理想、检测存在延迟等问题,提出了一种自适应颜色模型的火焰识别方法,以改善燃烧火焰的识别效果.首先,根据炉膛火焰燃烧特性设计含变量的自适应颜色模型,并采用最大类间方差法推导出自适应颜色模型中变量阈值的表达式;在此基础上,利用人类学习优化算法快速求解出最佳分割阈值,从而快速、精确地识别出炉膛火焰燃烧状况.最后,开展了仿真实验验证,结果表明所提出方法具有有效性与可行性.
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.
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.
引文
1 Zhou J L,Yue H,Zhang J F,et al.Iterative learning double closed-loop structure for modeling and controller design of output stochastic distribution control systems.IEEE Trans Control Syst Technol,2014,22:2261–2276
2 Chen H,Zhang X G,Hong P Y,et al.Recognition of the temperature condition of a rotary kiln using dynamic features of a series of blurry flame images.IEEE Trans Ind Inf,2016,12:148–157
3 Marjanovic A,Krstic M,Durovic Z,et al.Control of thermal power plant combustion distribution using extremum seeking.IEEE Trans Control Syst Technol,2017,25:1670–1682
4 Liu J X,Yang B,Cai X S.Research of combustion diagnostics and testing in boiler based on radiation spectroscopy.J Eng Thermophys,2015,36:2056–2060[刘家讯,杨斌,蔡小舒.辐射光谱法电站锅炉燃烧检测诊断研究.工程热物理学报,2015,36:2056–2060]
5 Liu Q,Wang S A,Zhang X H,et al.Flame recognition algorithm research under complex background.In:Proceedings of the 10th IEEE International Conference on Computer and Information Technology,Bradford,2010.503–510
6 Liu H,Wu Q S,Wang B,et al.BOF steelmaking endpoint real-time recognition based on flame multi-scale color difference histogram features weighted fusion method.In:Proceedings of the 35th Chinese Control Conference,Chengdu,2016.3659–3663
7 Wu Y Q,Zhu L,Zhou H C.State recognition of flame images based on krawtchouk moment and support vector machine.Proc CSEE,2014,34:734–740[吴一全,朱丽,周怀春.基于Krawtchouk矩和支持向量机的火焰状态识别.中国电机工程学报,2014,34:734–740]
8 An J Y,Ma X M.Simulation of flame video image combustion stability monitoring in thermal power plant.Comput Simul,2017,34:373–377[安静宇,马宪民.火电厂炉膛火焰视频图像燃烧稳定性监测.计算机仿真,2017,34:373 –377]
9 Xu L J,Tan C,Li X M,et al.Fuel-type identification using joint probability density arbiter and soft-computing techniques.IEEE Trans Instrum Meas,2012,61:286–296
10 Cheng Y,Sheng Y X,Chai L,et al.Burning state recognition using CW-SSIM index evaluation of color flame images.Control Decis Conf,2015,45:3609–3614
11 Wang Z C,Liu M,Dong M Y,et al.Riemannian alternative matrix completion for image-based flame recognition.IEEE Trans Circ Syst Video Technol,2017,27:2490–2503
12 Han T,Lin B W.Research on flame image recognition method in natural scene.In:Proceedings of the IEEE International Conference on Information and Automation,Ningbo,2016.1776–1780
13 Du D J,Qi B,Fei M R,et al.Quantized control of distributed event-triggered networked control systems with hybrid wired-wireless networks communication constraints.Inf Sci,2017,380:74–91
14 Du D J,Chen R,Fei M R,et al.A novel networked online recursive identification method for multivariable systems with incomplete measurement information.IEEE Trans Signal Inf Proc Netw,2017,3:744–759
15 Korhonen I,Ahola J.Microwave spectra inside the combustion chamber of a kraft recovery boiler-effects on communications.IET Sci Meas Technol,2017,11:740–745
16 Yin F,Luo Z H,Sun J D,et al.Online identification method of coal species in power plant boiler furnace based on local flame spectral characteristics.Proc CSEE,2016,36:5530–5539[尹峰,罗志浩,孙坚栋,等.电站锅炉炉内火焰局部光谱特征在线辩识煤种方法.中国电机工程学报,2016,36:5530–5539]
17 Liu P Y,Zhang D L,Zhang H,et al.Experimental and numerical studies on combustion characteristics of a 600MW supercritical arch-fired boiler equipped with slot burners before and after retrofit.Proc CSEE,2017,37:606–614[刘鹏远,张大龙,张海,等.缝隙式喷口600MW超临界W火焰锅炉改造前后燃烧特性的试验与数值计算研究.中国电机工程学报,2017,37:606–614]
18 Geng Q T,Yu F H,Zhao H W,et al.New algorithm of flame detection based on color features.J Jilin Univ(Eng Technol Edit),2014,44:1787–1892[耿庆田,于繁华,赵宏伟,等.基于颜色特征的火焰检测新算法.吉林大学学报(工学版),2014,44:1787–1892]
19 Yin F,Luo Z H,Li Y,et al.Coal type identification based on the emission spectra of a furnace flame.J Zhejiang Univ Sci A,2017,18:113–123
20 Li Q,Tang H,Chi J N,et al.Gesture segmentation with improved maximum between-cluster variance algorithm.Acta Autom Sin,2017,43:528–537[李擎,唐欢,迟健男,等.基于改进最大类间方差法的手势分割方法研究.自动化学报,2017,43:528–537]
21 Wang L,Pei J,Menhas M I,et al.A hybrid-coded human learning optimization for mixed-variable optimization problems.Knowl-Based Syst,2017,127:114–125
22 Wang L,Ni H Q,Yang R X,et al.An adaptive simplified human learning optimization algorithm.Inf Sci,2015,320:126 –139
23 Wang L,An L,Pi J X,et al.A diverse human learning optimization algorithm.J Global Optim,2017,67:1–41
24 Wang L,Yang R X,Pardalos P M,et al.An adaptive fuzzy controller based on harmony search and its application to power plant control.Int J Electr Power Energy Syst,2013,53:272–278
25 Wang L,Yang R X,Ni H Q,et al.A human learning optimization algorithm and its application to multi-dimensional knapsack problems.Appl Soft Comput,2015,34:736–743
26 Wang L,Ni H Q,Zhou W,et al.MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system.Eng Appl Artif Intel,2014,36:262–268
27 David O E,van den Herik H J,Koppel M,et al.Genetic algorithms for evolving computer chess programs.IEEE Trans Evol Comput,2014,18:779–789
28 Zeng N Y,Zhang H,Liu W B,et al.A switching delayed PSO optimized extreme learning machine for short-term load forecasting.Neurocomputing,2017,240:175–182
2 Chen H,Zhang X G,Hong P Y,et al.Recognition of the temperature condition of a rotary kiln using dynamic features of a series of blurry flame images.IEEE Trans Ind Inf,2016,12:148–157
3 Marjanovic A,Krstic M,Durovic Z,et al.Control of thermal power plant combustion distribution using extremum seeking.IEEE Trans Control Syst Technol,2017,25:1670–1682
4 Liu J X,Yang B,Cai X S.Research of combustion diagnostics and testing in boiler based on radiation spectroscopy.J Eng Thermophys,2015,36:2056–2060[刘家讯,杨斌,蔡小舒.辐射光谱法电站锅炉燃烧检测诊断研究.工程热物理学报,2015,36:2056–2060]
5 Liu Q,Wang S A,Zhang X H,et al.Flame recognition algorithm research under complex background.In:Proceedings of the 10th IEEE International Conference on Computer and Information Technology,Bradford,2010.503–510
6 Liu H,Wu Q S,Wang B,et al.BOF steelmaking endpoint real-time recognition based on flame multi-scale color difference histogram features weighted fusion method.In:Proceedings of the 35th Chinese Control Conference,Chengdu,2016.3659–3663
7 Wu Y Q,Zhu L,Zhou H C.State recognition of flame images based on krawtchouk moment and support vector machine.Proc CSEE,2014,34:734–740[吴一全,朱丽,周怀春.基于Krawtchouk矩和支持向量机的火焰状态识别.中国电机工程学报,2014,34:734–740]
8 An J Y,Ma X M.Simulation of flame video image combustion stability monitoring in thermal power plant.Comput Simul,2017,34:373–377[安静宇,马宪民.火电厂炉膛火焰视频图像燃烧稳定性监测.计算机仿真,2017,34:373 –377]
9 Xu L J,Tan C,Li X M,et al.Fuel-type identification using joint probability density arbiter and soft-computing techniques.IEEE Trans Instrum Meas,2012,61:286–296
10 Cheng Y,Sheng Y X,Chai L,et al.Burning state recognition using CW-SSIM index evaluation of color flame images.Control Decis Conf,2015,45:3609–3614
11 Wang Z C,Liu M,Dong M Y,et al.Riemannian alternative matrix completion for image-based flame recognition.IEEE Trans Circ Syst Video Technol,2017,27:2490–2503
12 Han T,Lin B W.Research on flame image recognition method in natural scene.In:Proceedings of the IEEE International Conference on Information and Automation,Ningbo,2016.1776–1780
13 Du D J,Qi B,Fei M R,et al.Quantized control of distributed event-triggered networked control systems with hybrid wired-wireless networks communication constraints.Inf Sci,2017,380:74–91
14 Du D J,Chen R,Fei M R,et al.A novel networked online recursive identification method for multivariable systems with incomplete measurement information.IEEE Trans Signal Inf Proc Netw,2017,3:744–759
15 Korhonen I,Ahola J.Microwave spectra inside the combustion chamber of a kraft recovery boiler-effects on communications.IET Sci Meas Technol,2017,11:740–745
16 Yin F,Luo Z H,Sun J D,et al.Online identification method of coal species in power plant boiler furnace based on local flame spectral characteristics.Proc CSEE,2016,36:5530–5539[尹峰,罗志浩,孙坚栋,等.电站锅炉炉内火焰局部光谱特征在线辩识煤种方法.中国电机工程学报,2016,36:5530–5539]
17 Liu P Y,Zhang D L,Zhang H,et al.Experimental and numerical studies on combustion characteristics of a 600MW supercritical arch-fired boiler equipped with slot burners before and after retrofit.Proc CSEE,2017,37:606–614[刘鹏远,张大龙,张海,等.缝隙式喷口600MW超临界W火焰锅炉改造前后燃烧特性的试验与数值计算研究.中国电机工程学报,2017,37:606–614]
18 Geng Q T,Yu F H,Zhao H W,et al.New algorithm of flame detection based on color features.J Jilin Univ(Eng Technol Edit),2014,44:1787–1892[耿庆田,于繁华,赵宏伟,等.基于颜色特征的火焰检测新算法.吉林大学学报(工学版),2014,44:1787–1892]
19 Yin F,Luo Z H,Li Y,et al.Coal type identification based on the emission spectra of a furnace flame.J Zhejiang Univ Sci A,2017,18:113–123
20 Li Q,Tang H,Chi J N,et al.Gesture segmentation with improved maximum between-cluster variance algorithm.Acta Autom Sin,2017,43:528–537[李擎,唐欢,迟健男,等.基于改进最大类间方差法的手势分割方法研究.自动化学报,2017,43:528–537]
21 Wang L,Pei J,Menhas M I,et al.A hybrid-coded human learning optimization for mixed-variable optimization problems.Knowl-Based Syst,2017,127:114–125
22 Wang L,Ni H Q,Yang R X,et al.An adaptive simplified human learning optimization algorithm.Inf Sci,2015,320:126 –139
23 Wang L,An L,Pi J X,et al.A diverse human learning optimization algorithm.J Global Optim,2017,67:1–41
24 Wang L,Yang R X,Pardalos P M,et al.An adaptive fuzzy controller based on harmony search and its application to power plant control.Int J Electr Power Energy Syst,2013,53:272–278
25 Wang L,Yang R X,Ni H Q,et al.A human learning optimization algorithm and its application to multi-dimensional knapsack problems.Appl Soft Comput,2015,34:736–743
26 Wang L,Ni H Q,Zhou W,et al.MBPOA-based LQR controller and its application to the double-parallel inverted pendulum system.Eng Appl Artif Intel,2014,36:262–268
27 David O E,van den Herik H J,Koppel M,et al.Genetic algorithms for evolving computer chess programs.IEEE Trans Evol Comput,2014,18:779–789
28 Zeng N Y,Zhang H,Liu W B,et al.A switching delayed PSO optimized extreme learning machine for short-term load forecasting.Neurocomputing,2017,240:175–182
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