基于差分量子粒子群算法的锅炉NO_x排放模型优化
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摘要
提出一种基于改进的差分量子粒子群(DEQPSO)算法,将其与超限学习机(ELM)相结合,以某1 000 MW超超临界机组锅炉燃烧系统为研究对象,建立了NO_x排放模型,采用现场样本数据测试所建模型的预测能力,并将该模型的预测结果与基本超限学习机以及引力搜索算法(GSA)、粒子群算法(PSO)和量子粒子群算法(QPSO)优化的超限学习机模型的预测结果进行了对比。结果表明:DEQPSO算法具有更好的参数优化性能,DEQPSO-ELM模型具有较强的泛化能力和良好的预测精度,为电站锅炉NO_x排放质量浓度预测提供了一种有效方法。
Taking the boiler combustion system of a 1 000 MW ultra supercritical unit as an object of study, a NO_x emission prediction model was established based on the combination of differential evolution quantum particle swarm optimization(DEQPSO) and extreme learning machine(ELM), of which the prediction ability was tested with field sample data, while the prediction results were successively compared with those of basic ELM models, and the ELM models optimized by gravitation search algorithm(GSA), particle swarm optimization(PSO) and quantum particle swarm optimization(QPSO), respectively. Results show that the DEQPSO algorithm has a strong capability in parameter optimization, and the DEQPSO-ELM model has strong generalization ability and high prediction accuracy, which may serve as a reference for NO_x emission prediction of power plant boilers.
Taking the boiler combustion system of a 1 000 MW ultra supercritical unit as an object of study, a NO_x emission prediction model was established based on the combination of differential evolution quantum particle swarm optimization(DEQPSO) and extreme learning machine(ELM), of which the prediction ability was tested with field sample data, while the prediction results were successively compared with those of basic ELM models, and the ELM models optimized by gravitation search algorithm(GSA), particle swarm optimization(PSO) and quantum particle swarm optimization(QPSO), respectively. Results show that the DEQPSO algorithm has a strong capability in parameter optimization, and the DEQPSO-ELM model has strong generalization ability and high prediction accuracy, which may serve as a reference for NO_x emission prediction of power plant boilers.
引文
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[9] XIE Xi, JIANG Weizhong, HE Nie, et al. Empirical study on how to set prices for cruise cabins based on improved quantum particle swarm optimization[J]. Computer and Information Science, 2016, 9(2): 82-90.
[10] HE Zhenzong, QI Hong, CHEN Qin, et al. Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements[J]. Particuology, 2016, 28: 6-14.
[11] HUANG Guangbin. Learning capability and storage capacity of two-hidden-layer feedforward networks[J]. IEEE Transactions on Neural Networks, 2003, 14(2): 274-281.
[12] SUN Jun, FANG Wei, WU Xiaojun, et al. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection[J]. Evolutionary Computation, 2012, 20(3): 349-393.
[13] NICKABADI A, EBADZADEH M M, SAFABAKHSH R. A novel particle swarm optimization algorithm with adaptive inertia weight[J]. Applied Soft Computing, 2011, 11(4): 3658-3670.
[14] TIAN Na, LAI C H, PERICLEOUS K, et al. Contraction-expansion coefficient learning in quantum-behaved particle swarm optimization[C]//2011 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science. Wuxi: IEEE, 2011: 303-308.
[15] PRICE K, STORN R M, LAMPINEN J A. Differential evolution: a practical approach to global optimization[M]. Berlin Heidelberg, Germany: Springer Verlag, 2005.
[16] 徐遥, 王士同. 引力搜索算法的改进[J]. 计算机工程与应用, 2011, 47(35): 188-192. XU Yao, WANG Shitong. Enhanced version of gravitational search algorithm: weighted GSA[J]. Computer Engineering and Applications, 2011, 47(35): 188-192.
[17] 吕游, 刘吉臻, 赵文杰, 等. 基于分段曲线拟合的稳态检测方法[J]. 仪器仪表学报, 2012, 33(1): 194-200. Lü You, LIU Jizhen, ZHAO Wenjie, et al. Steady-state detecting method based on piecewise curve fitting[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 194-200.
[2] 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. Lü 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.
[3] 刘彦华, 董泽. 风速及风电功率超短期动态选择线性组合预测[J]. 太阳能学报, 2016, 37(4): 1009-1016. LIU Yanhua, DONG Ze. Ultra-short-term wind speed and power forecast based on dynamic selective linear combined forecast[J]. Acta Energiae Solaris Sinica, 2016, 37(4): 1009-1016.
[4] 余廷芳, 耿平, 霍二光, 等. 基于智能算法的燃煤电站锅炉燃烧优化[J]. 动力工程学报, 2016, 36(8): 594-599, 607. YU Tingfang, GENG Ping, HUO Erguang, et al. Combustion optimization of a coal-fired boiler based on intelligent algorithm[J]. Journal of Chinese Society of Power Engineering, 2016, 36(8): 594-599, 607.
[5] 魏辉, 陆方, 罗永浩, 等. 燃煤锅炉高效、低NOx运行策略的研究[J]. 动力工程, 2008, 28(3): 361-366. WEI Hui, LU Fang, LUO Yonghao, et al. Research on operation strategy on reducing NOx emissions and improving efficiency of coal-fired boiler[J]. Journal of Power Engineering, 2008, 28(3): 361-366.
[6] HUANG Guangbin, ZHU Qinyu, SIEW C K. Extreme learning machine: theory and applications[J]. Neurocomputing, 2006, 70(1/2/3): 489-501.
[7] 高坤. 基于多核共空间模式的超限学习机聚类诊断方法研究[D]. 长沙: 湖南大学, 2015.
[8] 胡文斌, 王欢, 严丽平, 等. 混合指标量子群智能社会网络事件检测方法[J]. 软件学报, 2016, 27(11): 2747-2762. HU Wenbin, WANG Huan, YAN Liping, et al. Hybrid quantum swarm intelligence indexing for event detection in social networks[J]. Journal of Software, 2016, 27(11): 2747-2762.
[9] XIE Xi, JIANG Weizhong, HE Nie, et al. Empirical study on how to set prices for cruise cabins based on improved quantum particle swarm optimization[J]. Computer and Information Science, 2016, 9(2): 82-90.
[10] HE Zhenzong, QI Hong, CHEN Qin, et al. Retrieval of aerosol size distribution using improved quantum-behaved particle swarm optimization on spectral extinction measurements[J]. Particuology, 2016, 28: 6-14.
[11] HUANG Guangbin. Learning capability and storage capacity of two-hidden-layer feedforward networks[J]. IEEE Transactions on Neural Networks, 2003, 14(2): 274-281.
[12] SUN Jun, FANG Wei, WU Xiaojun, et al. Quantum-behaved particle swarm optimization: analysis of individual particle behavior and parameter selection[J]. Evolutionary Computation, 2012, 20(3): 349-393.
[13] NICKABADI A, EBADZADEH M M, SAFABAKHSH R. A novel particle swarm optimization algorithm with adaptive inertia weight[J]. Applied Soft Computing, 2011, 11(4): 3658-3670.
[14] TIAN Na, LAI C H, PERICLEOUS K, et al. Contraction-expansion coefficient learning in quantum-behaved particle swarm optimization[C]//2011 10th International Symposium on Distributed Computing and Applications to Business, Engineering and Science. Wuxi: IEEE, 2011: 303-308.
[15] PRICE K, STORN R M, LAMPINEN J A. Differential evolution: a practical approach to global optimization[M]. Berlin Heidelberg, Germany: Springer Verlag, 2005.
[16] 徐遥, 王士同. 引力搜索算法的改进[J]. 计算机工程与应用, 2011, 47(35): 188-192. XU Yao, WANG Shitong. Enhanced version of gravitational search algorithm: weighted GSA[J]. Computer Engineering and Applications, 2011, 47(35): 188-192.
[17] 吕游, 刘吉臻, 赵文杰, 等. 基于分段曲线拟合的稳态检测方法[J]. 仪器仪表学报, 2012, 33(1): 194-200. Lü You, LIU Jizhen, ZHAO Wenjie, et al. Steady-state detecting method based on piecewise curve fitting[J]. Chinese Journal of Scientific Instrument, 2012, 33(1): 194-200.