期刊文献+

求解高斯过程方程组预测煤元素含量研究 被引量:2

Prediction of elemental composition of coal by solving Gaussian process equations
下载PDF
导出
摘要 煤的元素组成是电厂能量转换过程效率分析的基本数据,煤质在线测量对电厂锅炉调整和控制有重要意义。本文给出一种根据煤的工业分析确定元素分析的全部成分的方法,这种方法首先建立煤成分关联方程,将关联方程的残差视为随机过程,根据高斯过程回归模型对残差进行预测;然后求解包含残差高斯过程的煤成分关联方程与全成分方程组成的方程组,得到元素含量。该方法不仅能够保证全成分的完整性,而且提高了预测的准确性,C、H、O和N元素含量的平均相对误差δMAPE分别为1.46%、6.34%、16.48%和12.86%。对于高硫煤,S含量预测误差较大,但对于常见含硫量的煤种,预测误差明显降低。 Elemental compositions of coal are essential for analyzing the overall process of energy conversion systems,it is vital for power plant operators or process control system to be simultaneously acquaint of properties of coal and take some adjustments to abate negative impacts due to variations in coal properties.A novel method was developed to predict elements of coal using proximate analysis.Firstly,correlations of compositional content of coal were established,and residuals of the correlations were regarded as random process and predicted according to Gaussian process regression model.The ultimate analysis was achieved by solving a set of simultaneous equations of compositional content of coal and complete component integrity equation.This method can not only ensure the integrity of the whole component,but also improve the accuracy of the component prediction.The mean relative errors δMAPE of C,H,O and N were 1.46%,6.34%,16.48% and 12.86%,respectively.For coals with high sulfur content,the prediction errors of S content were high,but for coals with common S content,the prediction error was significantly lower.
作者 刘福国 刘科 LIU Fuguo;LIU Ke(State Grid Shandong Electric Power Research Institute,Jinan 250003,China)
出处 《热力发电》 CAS CSCD 北大核心 2021年第7期70-77,共8页 Thermal Power Generation
基金 国网山东省电力公司电力科学研究院科技项目(ZY-2021-17)。
关键词 煤燃烧 工业分析 元素分析 元素含量 高斯过程 coal combustion proximate analysis ultimate analysis element content Gaussian process
  • 相关文献

参考文献7

二级参考文献76

共引文献148

同被引文献25

引证文献2

二级引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部