加入炉膛温度信息的电站锅炉燃烧优化
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摘要
在可预见未来的几十年内,煤炭仍将是我国主要的一次能源。以煤炭为主的一次能源格局,决定了在我国的电力工业中,燃煤火力发电占有主导地位。常规性的锅炉燃烧优化调整往往针对重要控制参数,给出特定工况点的优化策略,缺乏普遍适用性。基于模型预测和多目标寻优技术的燃烧优化系统可实现系统的闭环控制,达到经济与环保的多重优化目标,得到了广泛的应用。
本文利用一套炉膛燃烧温度场检测系统开展了锅炉多工况燃烧试验检测,获得了沿炉膛高度断面温度分布、燃烧效率和NOx排放量等数据。基于多工况燃烧试验检测结果,加入炉膛断面温度信息,建立了燃烧效率与NOx排放量的BP神经网络预测模型。模型预测的燃烧效率和NOx排放量与试验值的相对误差分别小于1%和5%。基于已建立的预测模型,分别采用遗传算法和颗粒群算法进行了锅炉高燃烧效率和低NOx排放量的单目标以及多目标燃烧优化。多目标优化结果表明,采用加入炉膛温度信息的预测模型的优化结果更加符合实际情况;在134MW、154MW、172MW时,遗传算法优化后的锅炉燃烧效率分别上升了0.84%、1.37%和2.61%,NOx排放量分别下降了18%、6%、18%。最后采用数值模拟方法对优化过程和结果进行了验证。断面平均温度数值模拟结果与试验值误差小于8%,表明数值模拟能够比较准确地模拟锅炉燃烧过程。锅炉NOx排放和燃烧效率数值模拟值能与优化结果的变化趋势较好吻合,也说明优化结果是合理的。
本文的研究表明:基于炉膛燃烧温度场可视化系统检测炉膛燃烧温度信息建立燃烧神经网络预测模型,能够更加准确地预测锅炉燃烧污染物排放过程;遗传算法的优化结果更接近实际情况,其结果能指导锅炉优化燃烧,也可为建立锅炉在线燃烧优化指导系统奠定基础。
Coal will remain China’s main primary energy in the foreseeable few decades. Coal-fired thermal power dominates the country’s power industry under the coal-based energy structure. Normally, boiler combustion system optimization focuses on important control parameters, spotting the optimization strategy on a specific operating point, but lacks general adaptability. Model prediction and multi-objective optimization based combustion optimization system can achieve system closed-loop control, and the multiple optimization objectives of economic and environmental, thus it has been widely applied.
In this paper, the furnace combustion temperature field detection system was used to detect combustion experiment under boiler’s multiple working conditions, and temperature distribution along the high degree cross-section of the furnace, combustion efficiency and NOx emission data were obtained. Adding the furnace cross-section temperature information to the multiple combustion detects results, the combustion efficiency and NOx emission prediction BP neural network model was established. The relative error between the combustion efficiency and NOx emissions predicted by the model and detect value was less than 1% and 5%. Based on the established prediction model, the genetic algorithm and particle swarm optimization algorithm were respectively used for the higher boiler combustion efficiency and lower NOx emission combustion optimization with single and multi-objective. Multi-objective optimization results show that adding information of the furnace temperature prediction model is more realistic; under 134MW, 154MW, 172MW, the optimized combustion efficiency rose by 0.84%, 1.37% and 2.62%, NOx emission decreased by 18%, 6%, 18% respectively. Finally, the optimization process and results were verified by numerical simulation method. The error of cross-section average temperature between simulation results and the detect were less than 8%, indicating that the numerical simulation can accurately simulate the combustion process. Boiler NOx emissions and combustion efficiency of the numerical simulation going in good agreement with the trend of the results of the optimization value also shows that the optimization results are reasonable.
This study showed that the neural network predict model of combustion based on furnace combustion temperature field visual system, detecting the furnace combustion temperature information, can predict the combustion process more accurately, and guide the boiler combustion optimization, and also lay the foundation for establishing on line combustion optimization system.
本文利用一套炉膛燃烧温度场检测系统开展了锅炉多工况燃烧试验检测,获得了沿炉膛高度断面温度分布、燃烧效率和NOx排放量等数据。基于多工况燃烧试验检测结果,加入炉膛断面温度信息,建立了燃烧效率与NOx排放量的BP神经网络预测模型。模型预测的燃烧效率和NOx排放量与试验值的相对误差分别小于1%和5%。基于已建立的预测模型,分别采用遗传算法和颗粒群算法进行了锅炉高燃烧效率和低NOx排放量的单目标以及多目标燃烧优化。多目标优化结果表明,采用加入炉膛温度信息的预测模型的优化结果更加符合实际情况;在134MW、154MW、172MW时,遗传算法优化后的锅炉燃烧效率分别上升了0.84%、1.37%和2.61%,NOx排放量分别下降了18%、6%、18%。最后采用数值模拟方法对优化过程和结果进行了验证。断面平均温度数值模拟结果与试验值误差小于8%,表明数值模拟能够比较准确地模拟锅炉燃烧过程。锅炉NOx排放和燃烧效率数值模拟值能与优化结果的变化趋势较好吻合,也说明优化结果是合理的。
本文的研究表明:基于炉膛燃烧温度场可视化系统检测炉膛燃烧温度信息建立燃烧神经网络预测模型,能够更加准确地预测锅炉燃烧污染物排放过程;遗传算法的优化结果更接近实际情况,其结果能指导锅炉优化燃烧,也可为建立锅炉在线燃烧优化指导系统奠定基础。
Coal will remain China’s main primary energy in the foreseeable few decades. Coal-fired thermal power dominates the country’s power industry under the coal-based energy structure. Normally, boiler combustion system optimization focuses on important control parameters, spotting the optimization strategy on a specific operating point, but lacks general adaptability. Model prediction and multi-objective optimization based combustion optimization system can achieve system closed-loop control, and the multiple optimization objectives of economic and environmental, thus it has been widely applied.
In this paper, the furnace combustion temperature field detection system was used to detect combustion experiment under boiler’s multiple working conditions, and temperature distribution along the high degree cross-section of the furnace, combustion efficiency and NOx emission data were obtained. Adding the furnace cross-section temperature information to the multiple combustion detects results, the combustion efficiency and NOx emission prediction BP neural network model was established. The relative error between the combustion efficiency and NOx emissions predicted by the model and detect value was less than 1% and 5%. Based on the established prediction model, the genetic algorithm and particle swarm optimization algorithm were respectively used for the higher boiler combustion efficiency and lower NOx emission combustion optimization with single and multi-objective. Multi-objective optimization results show that adding information of the furnace temperature prediction model is more realistic; under 134MW, 154MW, 172MW, the optimized combustion efficiency rose by 0.84%, 1.37% and 2.62%, NOx emission decreased by 18%, 6%, 18% respectively. Finally, the optimization process and results were verified by numerical simulation method. The error of cross-section average temperature between simulation results and the detect were less than 8%, indicating that the numerical simulation can accurately simulate the combustion process. Boiler NOx emissions and combustion efficiency of the numerical simulation going in good agreement with the trend of the results of the optimization value also shows that the optimization results are reasonable.
This study showed that the neural network predict model of combustion based on furnace combustion temperature field visual system, detecting the furnace combustion temperature information, can predict the combustion process more accurately, and guide the boiler combustion optimization, and also lay the foundation for establishing on line combustion optimization system.
引文
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[3] British Petroleum,2006.BP Statistical Review of World Energy 2006.www. bp. com/ centres / energy.
[4] China Statistics Bureau,1998 2001 2006 2007.China Energy Statistical Yearbook 1991–1996;2000–2002;2005;2006.China Statistics Press,Beijing.
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[12] Tronci S,Baratti R,Servida A.Monitoring pollutant emissions in a 4.8MW power plant through neural networks.Neurocompting 2002;43:3~15
[13] Chong AZS,Wilcox SJ,Ward J.Prediction of gaseous emissions from a chain grate stoker boiler using neural networks of ARX structure.IEE Proc Sci Measure Technol 2000;148(3):95~102
[14] Blonbou R,Laverdant A,Zaleski S et al.Active control of combustion instabilities on a Rijke tube using neural networks.Proc Combust Inst 2000;28:747~755
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[16] Zhu Q,Jones JM,Williams A et al.The predictions of coal/char combustion rate using an artifiial neural network approach.Fuel 1999;78(14):1755~1762
[17] A. Saptoro,H.M. Yao,M.O. Tade et al.Prediction of coal hydrogen content for combustion control in power utility using neural network approach.Chemometrics and Intelligent Laborotory Systems 2008;94: 149~159
[18] H.M. Yao , H.B. Vuthaluru . M.O. Tade , D. Djukanovic . Artificial neural network-based prediction of hydrogen content of coal in power station boilers.Fuel 2005;84: 1535~1542
[19]陈军.步进式加热炉燃烧过程智能控制:[硕士学位论文].长沙:中南大学,2004.
[20]王燕晋.模式识别与智能系统:[硕士学位论文].北京:华北电力大学,2006.
[21]云曦.基于预数值计算的电站煤粉锅炉燃烧优化控制专家系统研究:[硕士学位论文].保定:华北电力大学,2007.
[22]朱伟波.数字图像处理技术在锅炉燃烧诊断和优化中的应用:[硕士学位论文].保定:华北电力大学,2005.
[23]李慧娟.新型矿用热风炉燃烧检测与控制系统设计:[硕士学位论文].北京:华北电力大学,2008.
[24]刘佳.基于神经网络PID控制算法的热水锅炉燃烧控制的研究:[硕士学位论文].秦皇岛:燕山大学,2006.
[25]周欣.基于图像处理的炉膛燃烧火焰温度计算方法:[硕士学位论文].成都:四川大学,2005.
[26]张海清.生物质混煤燃烧及污染物排放特性研究:[硕士学位论文].济南:山东大学,2007.
[27]郭建民.基于数字图像处理技术的锅炉火焰检测与污染物排放特性研究:[博士学位论文].北京:中国科学研究院,2006.
[28] Michalewicz. Z.Genetic algorithms + data structures = evolution programs.3rd ed. Berbin: Springer,1996.
[29] Goldberg D.Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wwsley,1989.
[30] Polifke W,Geng W,Dobbeling K.Optimization of rate coefficients for simplified reaction meachanism with genetic algorithms.Combust Flame 1998;113:119~135
[31] Xiangping M,Huaguang Z,Wanyu T.A hybrid method of GA and BP for short-term economic dispatch of hydrothermal power systerms.Math Comput Simulation 2005;51:341~348
[32] Homma R,Chen JY.Combustion process optimization by genetic algorithms: reduction of NO2 emission via optimal postflame process.Proc Combust Inst 2000;28:2483~2489
[33] Harris SD,Elliott L,Ingham DB et al.The optimization of reaction rate parameters for chemical kinetic modeling of combustion using genetic algorithms.Comput Meth Apll Mech Engng 2000;190(8-10):1065~1090
[34] Chakravarthy SSS,Vohra AK,Gill BS.Predictive emission monitors (PEMS) for NOx generation in process heaters . Comput Chem Engng 2000 ;23(11/12):1649~1659
[35] Zhou H,Cen KF,Mao JB.Combining neural nerwork and genetic algorithms to optimize low NOx pulverized coal combustion.Fuel 2001;80(15):2163~2169
[36] Li-Gang Zheng,Hao Zhou,Ke-Fa Cen et al.A comparative study of optimization algorithms for low NOX combustion modification at a coal-fired utility boiler.Expert Systerms with Applications 2009;36: 2780~2793
[37]周怀春.炉内火焰可视化检测原理与技术.第1版.北京:科学与技术出版社,2005.163~165
[38]容銮恩,袁镇福,刘志敏等.电站锅炉原理.第1版.北京:中国电力出版社,1997.172~175
[39] Soteris A. Kalogirou.Artificial intelligence for the modeling and control of combustion processes: a review.Progress in Energy and Combustion Science 2006;29: 515~566
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