基于WOA-LSSVM的锅炉NO_x排放量预测模型
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摘要
精准可靠地预测锅炉NO_x排放量对电站锅炉低氮运行有着重要意义,为了提升模型的预测效果,提出一种基于鲸鱼优化算法-最小二乘支持向量机(WOA-LSSVM)的锅炉NO_x排放量预测建模方法。首先归一化处理初始样本数据,然后通过WOA算法对LSSVM中的核函数宽度和惩罚因子两个参数进行寻优求解,建立WOA-LSSVM黑箱模型,最终得到模型输出,同时将采用果蝇优化算法(FOA)、粒子群优化算法(PSO)优化参数建立的LSSVM预测模型和单一LSSVM预测模型作为对比研究。仿真结果表明,采用WOA优化的LSSVM模型在NO_x排放量预测方面明显优于其他选定模型,具有稳定且较高精度的仿真性能。
Given the significance of accurate and reliable prediction of boiler NO_x emission to low-nitrogen operation of power plant boilers, this paper proposed a predictive modeling methods of NO_x emission based on the whale optimization algorithm-least squares support vector machine(WOA-LSSVM). The first step was to normalize the initial sample data. Then this paper built a WOA-LSSVM black box model and obtained the model output by optimizing the kernel function width and penalty factor in LSSVM by WOA algorithm. At the same time, this paper adopted fruit fly optimization algorithm(FOA) and particle swarm optimization algorithm(PSO) in optimizing parameter to establish LSSVM prediction model. And this paper compared the improved LSSVM prediction model with single LSSVM prediction model. The simulation results showed that the WOS-optimized LSSVM model is superior to other selected models in NO_x emission prediction, and that it features stable and high-precision simulation performance.
Given the significance of accurate and reliable prediction of boiler NO_x emission to low-nitrogen operation of power plant boilers, this paper proposed a predictive modeling methods of NO_x emission based on the whale optimization algorithm-least squares support vector machine(WOA-LSSVM). The first step was to normalize the initial sample data. Then this paper built a WOA-LSSVM black box model and obtained the model output by optimizing the kernel function width and penalty factor in LSSVM by WOA algorithm. At the same time, this paper adopted fruit fly optimization algorithm(FOA) and particle swarm optimization algorithm(PSO) in optimizing parameter to establish LSSVM prediction model. And this paper compared the improved LSSVM prediction model with single LSSVM prediction model. The simulation results showed that the WOS-optimized LSSVM model is superior to other selected models in NO_x emission prediction, and that it features stable and high-precision simulation performance.
引文
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[7] 顾燕萍,赵文杰,吴占松.基于最小二乘支持向量机的电站锅炉燃烧优化[J].中国电机工程学报,2010,30(17):91-97.GU Yanping,ZHAO Wenjie,WU Zhansong.Combustion optimization for utility boiler based on least square support vector machine[J].Proceedings of the CSEE,2010,30(17):91-97.
[8] 张振星,孙保民,信晶,等.基于果蝇优化算法的锅炉高效率低NO_x燃烧建模[J].热力发电,2014,43(12):19-30.ZHANG Zhenxing,SUN Baomin,XING Jing,et al.Fruit fly optimization algorithm based high efficiency and low NOx combustion modeling for a boiler[J].Thermal Power Generation,2014,43(12):19-30.
[9] 高芳,翟永杰,卓越,等.基于共享最小二乘支持向量机模型的电站锅炉燃烧系统的优化[J].动力工程学报,2012,32(12):928-933.GAO Fang,ZHAI Yongjie,ZHUO Yue,et al.Combustion optimization for utility boilers based on sharing LSSVM model[J].Journal of Chinese Society of Power Engineering,2012,32(12):928-933.
[10] ZHU X,MA S Q,XU Q,et al.A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction [J].Journal of Mountain Science,2018,15(1):156-166.
[11] MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.
[12] SUN Y J,WANG X L,CHEN Y H,et al.A modified whale optimization algorithm for large-scale global optimization problems[J].2018,114:563-577.
[13] 张华磊,吕江毅,成林.基于WOA算法的电动汽车感应驱动系统控制器的参数整定[J/OL ].山东农业大学学报(自然科学版),2019,50(1):163-166.ZHANG Huanlei,LV Jiangyi,CHENG Lin.Parameter tuning of electric vehicle induction drive system controller based on WOA algorithm[J].Journal of Shandong Agricultural University(Natural Science Edition),2019,50(1):163-166.
[14] 滕德云,滕欢,潘晨,等.基于鲸鱼优化算法的无功优化调度[J].电测与仪表,2018,55(24):51-58.TENG Deyun,TENG Huan,PAN Chen,et al.Whale optimization algorithm based optimal reactive power dispatch[J].Electrical Measurement & Instrumentation,2018,55(24):51-58.
[15] ZHAO W J,ZHAO G,LV M,et al.Fuzzy optimization control for NOx emissions from power plant boilers based on nonlinear optimization[J].Journal of Intelligent & Fuzzy Systems,2015,29(6):2475-2481.
[16] 甄成刚,刘怀远.基于多模型聚类集成的锅炉烟气NO_x排放量预测模型[J].热力发电,2019,48(4):33-40.ZHEN Chenggang,LIU Huaiyuan.Prediction on NOx emission of coal-fired boiler based on multi-model clustering ensemble[J].Thermal Power Generation,2019,48(4):33-40.
[17] 赵文杰,吕猛.基于多LS-SVM集成模型的锅炉NOx排放量建模[J].电子测量与仪器学报,2016,30(7):1037-1044.ZHAO Wenjie,LV Meng.Modeling of NOx emission of boiler based on multiple LS-SVM integrated model[J].Journal of Electronic Measurement and Instrument,2016,30(7):1037-1044.
[18] 司刚全,李水旺,石建全,等.采用改进果蝇优化算法的最小二乘支持向量机参数优化方法[J].西安交通大学学报,2017,51(6):14-19.SI Gangquan,LI Shuiwang,SHI Jianquan,et al.Least Square Support Vector Machine parameter optimization method using improved fruit fly optimization algorithm[J].Journal of Xi′an Jiaotong University,2017,51(6):14-19.
[2] 丁续达,刘潇,金秀章.基于压缩感知最小二乘支持向量机的NO_x软测量模型[J].热力发电,2018,47(3):76-81.DING Xuda,LIU Xiao,JING Xiuzhang.A soft sensor model for NOx concentration based on CS-LSSVM[J].Thermal Power Generation,2018,47(3):76-81.
[3] 余廷芳,刘冉.基于RBF神经网络和BP神经网络的燃煤锅炉NO_x排放预测[J].热力发电,2016,45(8):94-98.YU Tingfang,LIU Ran.NOx emission prediction for coal-fired boiler based on RBF Neural network and BP Neural network[J].Thermal Power Generation,2016,45(8):94-98.
[4] 国家质量监督检查检疫总局环境保护部.锅炉大气污染物排放标准:GB 13271—2014[S].北京:中国环境科学出版社,2014.
[5] 吕游,刘吉臻,杨婷婷,等.基于PLS特征提取和LS-SVM结合的NOx排放特性建模[J].仪器仪表学报,2013,34(11):2418-2424.LV You,LIU Jizhen,YANG tingting,et al.NOx emission characteristic modeling based on feature extraction using PLS and LS-SVM[J].Chinese Journal of Scientific Instrument,2013,34(11):2418-2424.
[6] 李应保,王东风.一种改进型LSSVM模型在电站锅炉燃烧与优化中的应用[J].动力工程学报,2018,38(4):258-264.LI Yingbao,WANG Dongfeng.Application of an improved LSSVM in combustion modeling and optimization of utility boilers[J].Chinese Journal of Power Engineering,2018,38(4):258-264.
[7] 顾燕萍,赵文杰,吴占松.基于最小二乘支持向量机的电站锅炉燃烧优化[J].中国电机工程学报,2010,30(17):91-97.GU Yanping,ZHAO Wenjie,WU Zhansong.Combustion optimization for utility boiler based on least square support vector machine[J].Proceedings of the CSEE,2010,30(17):91-97.
[8] 张振星,孙保民,信晶,等.基于果蝇优化算法的锅炉高效率低NO_x燃烧建模[J].热力发电,2014,43(12):19-30.ZHANG Zhenxing,SUN Baomin,XING Jing,et al.Fruit fly optimization algorithm based high efficiency and low NOx combustion modeling for a boiler[J].Thermal Power Generation,2014,43(12):19-30.
[9] 高芳,翟永杰,卓越,等.基于共享最小二乘支持向量机模型的电站锅炉燃烧系统的优化[J].动力工程学报,2012,32(12):928-933.GAO Fang,ZHAI Yongjie,ZHUO Yue,et al.Combustion optimization for utility boilers based on sharing LSSVM model[J].Journal of Chinese Society of Power Engineering,2012,32(12):928-933.
[10] ZHU X,MA S Q,XU Q,et al.A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction [J].Journal of Mountain Science,2018,15(1):156-166.
[11] MIRJALILI S,LEWIS A.The whale optimization algorithm[J].Advances in Engineering Software,2016,95:51-67.
[12] SUN Y J,WANG X L,CHEN Y H,et al.A modified whale optimization algorithm for large-scale global optimization problems[J].2018,114:563-577.
[13] 张华磊,吕江毅,成林.基于WOA算法的电动汽车感应驱动系统控制器的参数整定[J/OL ].山东农业大学学报(自然科学版),2019,50(1):163-166.ZHANG Huanlei,LV Jiangyi,CHENG Lin.Parameter tuning of electric vehicle induction drive system controller based on WOA algorithm[J].Journal of Shandong Agricultural University(Natural Science Edition),2019,50(1):163-166.
[14] 滕德云,滕欢,潘晨,等.基于鲸鱼优化算法的无功优化调度[J].电测与仪表,2018,55(24):51-58.TENG Deyun,TENG Huan,PAN Chen,et al.Whale optimization algorithm based optimal reactive power dispatch[J].Electrical Measurement & Instrumentation,2018,55(24):51-58.
[15] ZHAO W J,ZHAO G,LV M,et al.Fuzzy optimization control for NOx emissions from power plant boilers based on nonlinear optimization[J].Journal of Intelligent & Fuzzy Systems,2015,29(6):2475-2481.
[16] 甄成刚,刘怀远.基于多模型聚类集成的锅炉烟气NO_x排放量预测模型[J].热力发电,2019,48(4):33-40.ZHEN Chenggang,LIU Huaiyuan.Prediction on NOx emission of coal-fired boiler based on multi-model clustering ensemble[J].Thermal Power Generation,2019,48(4):33-40.
[17] 赵文杰,吕猛.基于多LS-SVM集成模型的锅炉NOx排放量建模[J].电子测量与仪器学报,2016,30(7):1037-1044.ZHAO Wenjie,LV Meng.Modeling of NOx emission of boiler based on multiple LS-SVM integrated model[J].Journal of Electronic Measurement and Instrument,2016,30(7):1037-1044.
[18] 司刚全,李水旺,石建全,等.采用改进果蝇优化算法的最小二乘支持向量机参数优化方法[J].西安交通大学学报,2017,51(6):14-19.SI Gangquan,LI Shuiwang,SHI Jianquan,et al.Least Square Support Vector Machine parameter optimization method using improved fruit fly optimization algorithm[J].Journal of Xi′an Jiaotong University,2017,51(6):14-19.
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