LS-SVM and GM based dynamic soft sensor for coal level of ball mills

WANG Heng; HUA Guoran; JIA Minping; CHEN Zuoliang

English

您当前的位置：

首页 >

文章列表页 >

LS-SVM and GM based dynamic soft sensor for coal level of ball mills

Thermal Power Generation Vol. 44, Issue 1, Pages: 77-81(2015)

更新时间：2025-09-28

- LS-SVM and GM based dynamic soft sensor for coal level of ball mills
- 作者机构：
  
  1. 南通大学机械工程学院
  2. 东南大学机械工程学院
  3. 大唐南京下关发电厂
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM621;TP274
- Published：2015
- 稿件说明：
移动端阅览
WANG Heng, HUA Guoran, JIA Minping, et al. LS-SVM and GM based dynamic soft sensor for coal level of ball mills[J]. Thermal Power Generation, 2015, 44(1): 77-81.
DOI：

WANG Heng, HUA Guoran, JIA Minping, et al. LS-SVM and GM based dynamic soft sensor for coal level of ball mills[J]. Thermal Power Generation, 2015, 44(1): 77-81. DOI：

摘要

提出了一种基于最小二乘支持向量机(LS-SVM)和灰色模型(GM)的钢球磨煤机料位动态软测量方法

分析了料位的影响因素

确定了软测量模型的辅助变量;基于LS-SVM建立料位软测量静态模型

将静态模型测量结果与实际值比较

获得测量误差时间序列

并采用GM对其建模和预测;将预测的误差结果与静态模型输出进行叠加

实现对测量结果的动态校正。实际应用结果表明

该方法能够有效地反映料位的变化趋势和动态特性

比单纯LS-SVM模型测量具有更高的精度和适用性。

Abstract

A least squares support vector machine(LS-SVM)and grey model(GM)based dynamic soft sensor method for ball mills was proposed.By analyzing the factors affecting the coal level

the auxiliary variables of the soft sensor model were determined.The LS-SVM based soft sensor static model was established

of which the results were compared with that of the actual values.Thus the time measurement errors sequence was obtained and then modeled and predicted by the GM.Finally

the predictive error results were combined with the static model to realize dynamic correction.Application example shows this method can reflect the trend and dynamic characteristics of coal level effectively

which has a higher accuracy and applicability than the single LS-SVM model.

关键词

Keywords

references

Views

171

下载量

CSCD

Alert me when the article has been cited

提交

Publicity Resources

Modelling for unburned carbon content in fly ash from coal-fired boilers based on adaptive neuro-fuzzy inference system

Research on prediction model of heat consumption rate of steam turbine based on FOA-LSSVM

Robust least squares support vector machine based on online adaptive and its application

Adaptability analysis for semi-coke in different mill systems

Combustion optimization for CFB boiler based on least square support vector machine and modified fruit fly optimization algorithm

Related Author

严建华

池涌

倪明江

沈跃良

唐义军

尤海辉

YAN Jianhua

CHI Yong

Related Institution

Electric Power Research Institute of Guangdong Power Grid Corporation，Ltd.

State Key Laboratory of Clean Energy Utilization, Zhejiang University

School of Energy Power and Mechanical Engineering, North China Electric Power University

School of Control and Computer Engineering, North China Electric Power University

Xi'an Thermal Power Research Institute Co.,Ltd.

AI问答

⁰