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OALib Journal期刊
ISSN: 2333-9721
费用：99美元
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电站锅炉主要热工过程参数、软测量技术研究进展
, PP. 1-9
Keywords: 电站锅炉,热工过程参数,软测量,主元分析法,偏最小二乘法,神经网络法,支持向量机,模糊理论法
Abstract:
电站锅炉某些主要热工过程参数难以实现在线实时测量,从而制约了机组的高效、经济运行。为此,本文解析了基于统计分析的主元分析法和偏最小二乘法、基于人工智能的人工神经网络(ann)法、基于统计学习理论的支持向量机法以及模糊理论法的建模方法。并以球磨机负荷、煤质、风煤比、烟气含氧量、飞灰含碳量、汽包水位、主蒸汽温度、省煤器积灰、污染物排放量等参数为对象,综述了各种软测量技术的研究现状。结果显示:对于飞灰含碳量等呈非线性特征的变量,基于核主元分析(kpca)法建立其软测量模型,效果较好;当各变量的线性关联度高时,采用偏最小二乘回归(plsr)法建立其软测量模型更为有效;对于人工神经网络法,当实际样本空间超出训练样本空间区域时,模型输出误差较大,因此实际工业过程中需定时对基于ann法建立的模型参数进行校正;对于支持向量机(svm)法还无成熟的指导方法,基于经验数据则对模型精度的影响较大,最小二乘支持向量机(ls-svm)法的建模方法与传统svm法相比,训练时间更短,结果更具确定性,更适合工业在线建模;模糊理论法不需要被测对象的精确数学模型,但模糊系统本身不具有学习功能,如果能够将其与ann法等人工智能方法相结合,则可提高软测量的性能。因此,软测量技术的引入,使得难以在线测量的热工过程参数监测成为可能。
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