基于混合群智能算法优化的NO_X排放KELM模型设计

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英文篇名：Design of NO_X Emission KELM Model Based on Hybrid Swarm Intelligent Algorithm Optimization 作者：高学伟 ; 付忠广 ; 谢鲁冰 ; 王圣毫 ; 王树成 英文作者：GAO Xuewei;FU Zhongguang;XIE Lubing;WANG Shenghao;WANG Shucheng;School of Energy Power and Mechanical Engineering,North China Electric Power University;Simulation Center,Shenyang Institute of Engineering; 关键词：NO_X排放 ; 选择性催化还原 ; 极限学习机 ; 核函数 ; 蚁群优化算法 ; 粒子群优化算法 ; 数据建模 ; 动态模型 英文关键词：NO_X emission;;Selective catalytic reduction(SCR);;Limit learning machine;;Kernel function;;Ant colony optimization(ACO) algorithm;;Particle swarm optimization(PSO) algorithm;;Data modeling;;Dynamic model 中文刊名：自动化仪表 英文刊名：Process Automation Instrumentation 机构：华北电力大学能源动力与机械工程学院;沈阳工程学院仿真中心; 出版日期：2019-08-20 出版单位：自动化仪表 年：2019 期：08 语种：中文; 页：36-41 页数：6 CN：31-1501/TH ISSN：1000-0380 分类号：X773;TP18

摘要

燃煤电站脱硝系统在变负荷工况下具有非线性、大滞后的特性,传统的控制方式很难保证喷氨量的精确控制。随着燃煤发电厂超低排放标准的实施,有必要对脱硝系统进行运行优化。通过挖掘海量脱硝系统的历史运行数据,提出一种基于混合群智能算法优化的核极限学习机NO_X排放动态预测模型。首先,对选择性催化还原(SCR)脱硝反应系统进行理论分析和实际运行研究,研究了采用核函数代替极限学习机中隐含层节点的显式映射的方法,从而无需事先给定隐含层节点数。然后,采用混合蚁群和粒子群优化的混合智能算法,对核极限学习机的学习参数进行优化。最后,以某电站锅炉脱硝系统为例,利用提出的方法进行验证,得到较高的建模精度。该研究为下一步脱硝系统控制优化打下良好基础。
        The denitrification system of coal-fired power station has nonlinear and large hysteresis characteristics under variable load conditions; traditional control methods are difficult to guarantee precise control of ammonia injection.With the implementation of ultra-low emission standards for coal-fired power plants,it is necessary to optimize the operation of denitrification system.By mining the historical operation data of mass denitrification system,a dynamic prediction kernel limit learning machine(KELM) NO_X emission model based on hybrid swarm intelligence algorithm optimization is proposed.Firstly,the theoretical analysis and practical operation of SCR denitrification reaction system are studied.The explicit mapping method of using kernel function instead of hidden layer nodes in limit learning machine is studied,so that the number of hidden layer nodes need not be given beforehand.Then,the parameters of kernel limit learning machine are optimized by hybrid ant colony optimization and particle swarm optimization.Finally,the parameters of kernel limit learning machine are optimized by using hybrid intelligent algorithm of ant colony optimization and particle swarm optimization.With a certain power plant boiler denitrification system as example,using the proposed method to verify,the higher accuracy of modelling is obtained,which lays a good foundation for optimization of denitrification system control in future.

引文

[1] 秦天牧,刘吉臻,杨婷婷,等.火电厂SCR烟气脱硝系统建模与运行优化仿真[J].中国电机工程学报,2016,36(10):2699- 2703.
    [2] 赵文杰,张楷.基于互信息变量选择的SCR烟气脱硝系统非线性自回归神经网络建模[J].热力发电,2018,47(9):22- 26.
    [3] 秦天牧,林道鸿,杨婷婷,等.SCR烟气脱硝系统动态建模方法比较[J].中国电机工程学报,2017,37(10):2913- 2919.
    [4] 姚楚,龙东腾,吕游,等.火电厂SCR 脱硝系统机理建模与控制研究[J].热能动力工程,2018,33(5):78- 84.
    [5] 方贤,铁治欣,崔仕文,等.基于粒子群优化双支持向量机的SCR烟气脱硝效率预测模型[J].热力发电,2018,47(1):53- 58.
    [6] 刘吉臻,秦天牧,杨婷婷,等.基于自适应多尺度核偏最小二乘的SCR烟气脱硝系统建模[J].中国电机工程学报,2015,35(23):127- 132.
    [7] 刘吉臻,秦天牧,杨婷婷,等.基于偏互信息的变量选择方法及其在火电厂SCR系统建模中的应用[J].中国电机工程学报,2016,36(9):126- 131.
    [8] 秦天牧,刘吉臻,方连航,等.基于改进偏互信息的火电厂SCR脱硝系统建模[J].动力工程学报,2016,36(9):57- 62.
    [9] 秦天牧.燃煤电站SCR烟气脱硝系统建模与喷氨量优化控制[D].北京:华北电力大学,2017.
    [10]牛培峰,王枭飞,刘楠,等.ASOS_ELM建模方法及在汽轮机热耗率预测中的应用[J].化工学报,2018,69(9):3924- 3931.
    [11]许爱东,李昊飞,程乐峰,等.PCA_PSO_ELM配网供电可靠性预测模型[J].哈尔滨工程大学学报,2018,39(6):166- 172.
    [12]HUANG G B.An insight into extreme learning machines:Random neurons,random features and kernels [J].Cognitive Computation,2014,6(3):376- 390.
    [13]杨锡运,关文渊,刘玉奇,等.基于粒子群优化的核极限学习机模型的风电功率区间预测方法[J].中国电机工程学报,2015,35(9):146- 153.
    [14]李春祥,丁晓达,叶继红.基于混合蚁群和粒子群优化LSSVM的脉动风速预测[J].振动与冲击,2016,35(21):131-136.
    [15]高学伟,付忠广,刘炳含,等.SCR脱硝系统PCA- HPSO- SVR大数据建模研究[J].自动化仪表,2017,38(7):15- 19.
    [16]张铁虎,俞经虎,王琨.基于ACO- PSO算法的点胶路径规划与分析[J].计算机应用,2016,36(S2):89- 92.