焦炉控制参数与一氧化碳排放关系建模与分析
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摘要
目前降低焦炉烟囱CO排放的方法多注重于改进加热炉结构、管理维护等方面,消耗大量人力物力。由于焦炉燃烧是具有强耦合、多输入、非线性系统等特点的复杂工业过程,因此研究主要在煤气组成成分含量基本不变的情况下,通过PSO(粒子群优化算法)优化RBF(径向基函数)神经网络对烟道压力、烟道温度、氧气烟囱排放浓度等焦炉燃烧控制参数与CO烟囱排放浓度的关系进行建模与分析,找出影响CO排放最大的控制参数,进而通过控制该参数达到降低CO排放的目的。实验结果证明氧气烟囱排放浓度对CO排放影响最大。
Now the methods of reducing CO emission need to consume a large amount of resources to transform the structure.The coke oven combustion is a complex industrial process,which has characteristics such as strong coupling,multi-input,nonlinear and so on.So under the condition that the gas composition content is almost the same,the relations between the coke oven combustion control parameters like the flue pressure,the flue temperature,the oxygen emission concentration of chimney and the carbon monoxide emission concentration of chimney are modeled and then analyzed based on the algorithm of PSO(particle swarm optimization algorithm) optimization RBF(radial basis function) neural work.Then the most important factor to achieve the purpose of reducing CO is found.Experimental results show that the greatest factor of CO emission is the oxygen concentration.
Now the methods of reducing CO emission need to consume a large amount of resources to transform the structure.The coke oven combustion is a complex industrial process,which has characteristics such as strong coupling,multi-input,nonlinear and so on.So under the condition that the gas composition content is almost the same,the relations between the coke oven combustion control parameters like the flue pressure,the flue temperature,the oxygen emission concentration of chimney and the carbon monoxide emission concentration of chimney are modeled and then analyzed based on the algorithm of PSO(particle swarm optimization algorithm) optimization RBF(radial basis function) neural work.Then the most important factor to achieve the purpose of reducing CO is found.Experimental results show that the greatest factor of CO emission is the oxygen concentration.
引文
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[2]金珂,冯妍卉,张欣欣,等.焦炉加热系统流量分配的数值仿真[J].钢铁,2013,48(9):16-20.Jin K,Feng Y H,Zhang X X,et al.Numerical analysis on flow distribution in regenerator of coke oven heating system[J].Iron and Steel,2013,48(9):16-20.
[3]布焕存.蓄热式高温空气燃烧技术的应用[J].钢铁研究学报,2005,17(5):1-6.Bu H C.Application of heat storage high temperature air combustion technology[J].Journal of Iron and Steel Research,2005,17(5):1-6.
[4]张俊春.多孔介质燃烧处理低热值气体及燃烧不稳定性研究[C].浙江大学,2014.Zhang J C.Porous media combustion for low calorific gases and combustion instabilities[C].Zhejiang University,2014.
[5]蔡九菊,王建军,陈春霞,等.钢铁企业余热资源的回收与利用[J].钢铁,2007,42(6):1-7.Cai J J,Wang J J,Chen C X,et al.Recovery of residual-heat integrated steelworks[J].Iron and Steel,2007,42(6):1-7.
[6]郭伟,韩丹丹,徐金成等.基于粒子群优化的分数阶PID预测函数参数整定[J].控制工程,2014,21(1):70-73.Guo W,Han D D,Xu J C,et al.Fractional order PID predictive functional control parameter tuning based on particle swarm optimization[J].Control Engineering of China,2014,21(1):70-73.
[7]李砚浓,李汀兰,姜艺等.基于RBF神经网络自适应PID四旋翼飞行器控制[J].控制工程,2016,23(3):378-382.Li Y N,Li T L,Jiang Y,et al.Adaptive PID control of quadrotor based on RBF nrural network[J].Control Engineering of China,2016,23(3):378-382.
[8]王伟.炼焦过程综合生产目标的智能预测与协调优化研究[C].中南大学,2011.Wang W.Intelligent prediction and coordinative optimization of comprehensive production targets in the coking process[C].Zhongnan University,2011.
[9]张俊春,程乐鸣,王勤辉,等.低热值气体蓄热燃烧产生高温气体[J].浙江大学学报,2014,4(8):1534-1540.Zhang J C,Cheng LM,Wang Q H,et al.Investigation of generating high temperature gas from low heating-value gas[J].Journal of Zhejiang University,2014,48(8):1534-1540.
[10]薛占强,刘尚.7m焦炉碳化室底部炉体温度测温方式及温度特性研究[J].冶金自动化,2013,37(5):67-70,75.Xue Z Q,Liu S.Research on the temperature characteristics of 7m coke oven based on the measurement method of bottom at coking chamber[J].Metallurgical Industry Automation,2013,37(5):67-70,75.
[11]余杨,李晓斌,王建华,等.煤调湿控制系统的智能建模与优化方法研究[J].科学技术与工程,2013,30(13):8938-8944.Yu Y,Li X B,Wang J H,et al.Research on the intelligent modeling and optimization method of coal moisture control system[J].Science technology and engineering,2013,30(13):8938-8944.
[12]李少华,许乐飞,宋东辉,等.基于BP神经网络的锅炉烟气中CO的测量[J].东北电力大学学报,2014,34(3):85-88.Li S H,Xu L F,Song D H,et al.Measurement of carbon monoxide in boiler flue gas based on BP neural network[J].Journal of northeast Dianli University,2014,34(3):85-88.
[13]张立华,谭德宽,王兵.加热炉基于CO控制的燃烧优化技术的应用[J].石油化工安全环保技术,2013,29(3):1-4Zhang L H,Tan D K,Wang B.Application of combustion optimization technonlogy in heating furnace based on CO control[J].Petrochemical safety and environmental protection technology,2013,29(3):1-4.
[14]张安强,冯妍卉,张欣欣,等.焦炉燃烧室-炭化室热过程数值模拟及解耦算法.化工学报[J],2012,63(2):455-462.Zhang A Q,Feng Y H,Zhang X X,et al.Decoupling numerical methods for simulation of thermal process in coupled combusion and carbonization chambers of coke oven[J].CIESC Journal,2012,63(2):455-462.
[15]王伟,吴敏,雷琪,等.炼焦生产过程质量能耗的集成优化控制[J].化工学报,2008,59(7):1749-1754.Wang W,Wu M,Lei Q,et al.Integrated optimal control of coke quality,coke yield and energy consumption for coking process[J].Journal of chemical industry and engineering(China),2008,59(7):1749-1754
[16]雷琪,吴敏.经济性能评估的炼焦生产过程优化运行方法[J].上海交通大学学报,2012,46(12):1914-1919,1925.Lei Q,Wu M.Optimization operation for coking process based on economic performance assessment[J].Journal of Shanghai jiaotong University,2012,46(12):1914-1919,1925