全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
化工学报  2015 

基于改进支持向量机的高炉一氧化碳利用率预测方法

DOI: 10.11949/j.issn.0438-1157.20141482, PP. 206-214

Keywords: 高炉,一氧化碳,建模,支持向量机,预测,自适应粒子群

Full-Text   Cite this paper   Add to My Lib

Abstract:

高炉冶炼是一个具有非线性、大时滞、大噪声、分布参数等特征的高度复杂生产过程。针对目前高炉现场以焦比为能耗评价指标却无法提供实时指导的问题,研究以一氧化碳利用率为能耗评价指标,提出一种基于改进支持向量机的高炉一氧化碳利用率预测方法。首先分析高炉炼铁过程机理,结合互信息法得出影响一氧化碳利用率的相关操作因素。然后鉴于生产数据含噪高的特点,采用小波去噪方法去除数据噪声干扰,并且利用灰色相对关联度分析方法对操作参数进行时序配准,消除时滞影响,建立高炉一氧化碳利用率预测模型。在建模过程中,将自适应粒子群与支持向量机回归方法相结合,以克服模型参数选择的随机性,提高了模型预测精度。现场实际数据的预测结果表明所提出方法的有效性,能够实时精确地预测高炉一氧化碳利用率,为后续高炉的优化操作和节能减排提供了及时有效的决策支持。

References

[1]  Wei Hangyu (魏航宇). Analysis and practice of utilization rate of No.5 BF gas//Practical New Technology and Equipment of Efficient Ironmaking and Raw Material [C]. Hangzhou, 2013: 87-90
[2]  Han Min (韩敏), Liu Xiaoxin (刘晓欣). An extreme learning machine algorithm based on mutual information variable selection [J]. Control and Decision (控制与决策), 2014, 29 (9): 1576-1580
[3]  Gao Chuanhou (郜传厚), Jian Ling (渐令), Chen Jiming (陈积明), Sun Youxian (孙优贤). Data-driven modeling and predictive algorithm for complex blast furnace ironmaking process [J]. Acta Automatica Sinic (自动化学报), 2009, 35 (6): 725-730
[4]  Zeng Jiusun (曾九孙), Liu Xiangguan (刘祥官), Luo Shihua (罗世华), et al. Application of principal component regression and partial least square in blast furnace iron-making [J]. Journal of Zhejiang University: Sciences Edition (浙江大学学报: 理学版), 2009, 36 (1): 33-36
[5]  Madadi Z, Anand G V, Premkummar A B. Signal detection in generalizaed Gaussian noise by nonlinear wavelet denoising [J]. IEEE Transactions on Circuits and Systems, 2013, 60 (11): 2973-2986
[6]  Shi Jie, Ding Zhaohao, Lee Wei-Jen. Hybrid forecasting model for very-short termwind power forecasting based on grey relational analysis and wind speed distribution features [J]. IEEE Transactions on Smart Grid, 2014, 5 (1): 521-526
[7]  Wang Zhanneng (王占能), Xu Zuhua (徐祖华), Zhao Jun (赵均), Shao Zhijiang (邵之江). Coal-fired power plant boiler combustion process modeling based on support vector machine and load data division [J]. CIESC Journal (化工学报), 2013, 64 (12): 4496-4502
[8]  Zhou You (周游), Zhao Chengye (赵成业), Liu Xinggao (刘兴高). An iteratively adaptive particle swarm optimization approach for solving chemical dynamic optimization problems [J]. CIESC Journal (化工学报), 2014, 65 (4): 1296-1302
[9]  Zhan Zhihui, Zhang Jun, Li Yun. Adaptive particle swarm optimization [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 2009, 39 (6): 1362-1381
[10]  Pan Hao (潘昊), Yu Haibin (于海滨), Fan Mingzhe (苑明哲). Modeling and analysis of energy using efficiency of the blast furnace [J]. Manufacturing Automation (制造业自动化), 2011, 33 (23): 142-144
[11]  Liu Hui (刘慧), Li Peiran (李沛然), Bao Zhejing (包哲静). Intelligent predictive modeling of blast furnace system [J]. Journal of Central South University: Science and Technology (中南大学学报:自然科学版), 2012, 43 (5): 1787-1794
[12]  Fan Zhigang (范志刚), Qiu Guibao (邱贵宝), Jia Juanyu (贾娟鱼). Method to predict the coke rate based on BP neural network [J]. Journal of Chongqing University: Science and Technology (重庆大学学报:自然科学版), 2002, 25 (6): 85-91
[13]  Chen Tiejun (陈铁军), Chen Huafang (陈华方). Research on coke ratio and temperature control algorithm in puddling based on chain system [J]. Control and Instruments in Chemical Industry (化工自动化及仪表), 2010, 37 (4): 26-28
[14]  Chi Yanbin, Yang Shuangping, Dong Jie. Research on technology of blast furnace optimization of operation principle [J]. Advanced Materials Research, 2012, 516/517: 401-403
[15]  Zhou Chuandian (周传典). Blast Furnace Ironmaking Production of Technical Manual (高炉炼铁生产技术手册)[M]. Beijing: Metallurgical Industry Press, 2005: 174-177
[16]  Lahm A H. Calculation and Analysis of Modern Blast Furnace Process (现代高炉过程的计算分析)[M]. Wang Xiaoliu (王筱留), trans.Beijing: Metallurgical Industry Press, 1987: 266-289
[17]  Na Shuren (那树人). Ironmaking Calculation Analysis (炼铁计算辨析)[M]. Beijing: Metallurgical Industry Press, 2010:161-199
[18]  Xiang Zhongyong (项钟庸), Wang Xiaoliu (王筱留), Yin Han (银汉). More discussion on evaluation method for productive efficiency of ironmaking blast furnace [J]. Iron and Steel, 2013, 48 (3): 86-91
[19]  Hu Zhenggang (胡正刚). Strategy for improvement in utilization rate of No.5 BF gas in WISCO and its practice [J]. Wisco Technology (武钢技术), 2012, 50 (2): 8-11
[20]  Gao Chuanhou, Jian Ling, Luo Shihua. Modeling of the thermal state change of blast furnace hearth with support vector machines [J]. IEEE Transactions on Industrial Electronics, 2012, 50 (4): 725-730
[21]  Gao Chuanhou, Jian Ling, Liu Xueyi, Chen Jiming, Sun Youxian. Data-driven modeling based on volterra series for multidimensional blast furnace system [J]. IEEE Transactions on Neural Networks, 2011, 22 (12): 2272-2283

Full-Text

Contact Us

[email protected]

QQ:3279437679

WhatsApp +8615387084133