|
|
|
| Optimization for NOx Prediction Model from Boilers Based on GSA-PELM |
| NIU Pei-feng,SHI Chun-jian,LIU Nan,CHANG Ling-fang,ZHANG Xian-chen |
| Key Lab of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao, Hebei 066004, China |
|
|
|
|
Abstract As to precisely predict the NOx emission from boiler,an integrated modeling method was establisbcd based on gravitation search algorithm (GSA) and parallel extreme learning machine (PELM),and used the model to forecast the power plant boiler NOx emission concentration.As the generalization ability and accuracy of PELM rely on the choice of weight,using the gravitation search algorithm optimization PELM weights.Test sample data were collected in a coal-fired power plant 300 MW circulating fluidized bed boiler under different conditions,the simulation consequences showed that compared with PELM model,ELM model,GSA-LSSVM and GSA-ELM model,GSA-PELM model the accuracy could be improved by more than 9 orders of magnitude and can be used more effectively and accurately to predict NOx emission concentration of boilers in thermal power plants.
|
|
Received: 26 May 2017
Published: 05 September 2018
|
|
|
| Fund:the National Natural Science Foundation of China |
|
|
|
［1］肖海平, 张千, 王磊, 等.燃烧调整对NOx排放及锅炉效率的影响［J］.中国电机工程学报,2011,31(8):1-6.
［2］牛培峰,王丘亚,马云鹏, 等.基于量子自适应鸟群算法的锅炉NOx排放特性研究［J］.计量学报,2017,38(6):770-775.
［3］欧阳子区, 朱建国, 吕清刚.无烟煤粉经循环流化床预热后燃烧特性及NOx排放特性实验研究［J］.中国电机工程学报,2014,34(11):1748-1754.
［4］牛培峰, 麻红波, 李国强, 等.基于支持向量机和果蝇优化算法的循环流化床锅炉NOx排放特性研究［J］.动力工程学报,2013,33(4):267-271.
［5］赵发家, 董志乾, 王家万, 等.300 MW循环流化床锅炉运行优化［J］.中国电力,2008,41(2):30-33.
［6］吕清刚, 雍玉梅, 那永洁, 等.循环流化床燃煤锅炉的SO2和NOx排放的试验和数值计算［J］.中国电机工程学报,2005,25(1):100-102.
［7］牛培峰.基于风驱动算法的锅炉NOx排放模型优化［J］.动力工程学报,2016,36(9):732-738
［8］牛培峰, 刘超, 李国强, 等.汽轮机热耗率多模型建模方法研究［J］.计量学报,2015,36(3):251-255.
［9］牛培峰, 麻红波, 李国强, 等.基于GSA-SVM的循环流化床锅炉NOx排放特性模型［J］.计量学报,2013,34(6):602-606.
［10］牛培峰, 王培坤, 李国强, 等.基于自由搜索算法和支持向量机的燃煤锅炉NOx建模与优化［J］.计量学报,2014,35(6):627-631.
［11］高海兵,高亮, 周驰, 等.基于粒子群优化的神经网络训练算法研究［J］.电子学报,2004,32(9):1572-1574
［12］Das S, Suganthan P N. Differential Evolution: A Survey of the State-of-the-Art［J］. IEEE Transactions on Evolutionary Computation,2011,15(1): 4-31 .
［13］Huang G B, Zhu Q Y, Siew C K. Extreme learning machine: theory and applications［J］. Neurocomputing,2006,70(1-3): 489-501.
［14］Li G Q ,Qi X B, Chen B, et al. Fast learning network with parallel layer perceptrons［J］. Neural Processing Letters,2017,13(3):237-251.
［15］Qu B Y, Lang B F, Liang J J, et al. Two-hidden-layer extreme learning machine for regression and classification［J］. Neurocomputing,2016,175(Part A):826-834.
［16］Huang G B, Wang D H, Lan Y. Extreme learning machines: a survey［J］. International Journal of Machine Learning and Cybernetics,2011,2(2):107-122.
［17］mer F E. Forecasting electricity load by a novel recurrent extreme learning machines approach［J］. International Journal of Electrical Power & Energy Systems,2016,78:429-435.
［18］Zhang H G, Yin Y X, Zhang S. An improved ELM algorithm for the measurement of hot metal temperature in blast furnace［J］. Neurocomputing,2015,174(Part A):232-237.
［19］Li G Q, Niu P F, Duan X L, et al. Fast learning network: a novel artificial neural network with a fast learning speed［J］. Neural computing & applications,2014,24(7-8):1683 - 1695.
［20］李国强.新型人工智能技术研究及其在锅炉燃烧优化中的应用［D］.秦皇岛:燕山大学,2013.
［21］Esmat R, Hossein N, Saryazdi S. GSA: A Gravitational Search Algorithm［J］. Information Sciences,2009,179(13): 2232-2248.
［22］牛培峰, 马云鹏, 张京,等.基于相关向量机的电站锅炉NOx燃烧优化［J］.计量学报,2016,37(2):191-196.
［23］廖子昱.循环流化床锅炉N2O生成与控制研究［D］.杭州:浙江大学,2011.
［24］马宏明, 翟晓敏.某电厂300MW亚临界循环流化床锅炉钢结构设计介绍［J］.锅炉制造,2014,(3):16-17,20.
［25］段景卫, 赵志丹, 刘爱军, 等.某300 MW循环流化床锅炉结焦原因分析［J］.热力发电,2011,40(1):45-47. |
|
|
|
2018, Vol. 39 
